diff --git a/HEADER.md b/HEADER.md index cb3fda0..e3de85c 100644 --- a/HEADER.md +++ b/HEADER.md @@ -1,6 +1,6 @@ # boardgame-research [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) -This is a list of boardgame research. They are primarily related to "solving/playing/learning" games (by various different approaches), or +This is a list of boardgame related research papers, code, blog posts, and other media. The list primarily includes research on "solving/playing/learning" games (by various different approaches), or occasionaly about designing or meta-aspects of the game. This doesn't cover all aspects of each game (notably missing social-science stuff), but should be of interest to anyone interested in boardgames and their optimal play. While there is a ton of easily accessible research on games like Chess and Go, finding prior work on more contemporary games can be a bit hard. This list focuses on the latter. If you are interested in well-researched @@ -17,4 +17,4 @@ See Import instructions here: https://www.zotero.org/support/kb/importing_standa If you aren't able to access any paper on this list, please [try using Sci-Hub](https://en.wikipedia.org/wiki/Sci-Hub) or [reach out to me](https://captnemo.in/contact/). - \ No newline at end of file + diff --git a/README.md b/README.md index 90ec0ef..3f5e1da 100644 --- a/README.md +++ b/README.md @@ -29,6 +29,7 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub]( - [Dominion](#dominion) - [Frameworks](#frameworks) - [Game Design](#game-design) +- [General Gameplay](#general-gameplay) - [Hanabi](#hanabi) - [Hive](#hive) - [Jenga](#jenga) @@ -66,101 +67,107 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub]( # 2048 -- [Systematic Selection of N-Tuple Networks for 2048](https://doi.org/10.1007%2F978-3-319-50935-8_8) (bookSection) -- [Systematic selection of N-tuple networks with consideration of interinfluence for game 2048](https://doi.org/10.1109%2Ftaai.2016.7880154) (conferencePaper) -- [An investigation into 2048 AI strategies](https://doi.org/10.1109%2Fcig.2014.6932920) (conferencePaper) -- [Threes!, Fives, 1024!, and 2048 are Hard](http://arxiv.org/abs/1505.04274v1) (journalArticle) -- [Making Change in 2048](http://arxiv.org/abs/1804.07396v1) (journalArticle) -- [Analysis of the Game "2048" and its Generalization in Higher Dimensions](http://arxiv.org/abs/1804.07393v2) (journalArticle) -- [Multi-Stage Temporal Difference Learning for 2048-like Games](http://arxiv.org/abs/1606.07374v2) (journalArticle) -- [2048 is (PSPACE) Hard, but Sometimes Easy](http://arxiv.org/abs/1408.6315v1) (journalArticle) -- [Temporal difference learning of N-tuple networks for the game 2048](http://ieeexplore.ieee.org/document/6932907/) (conferencePaper) -- [On the Complexity of Slide-and-Merge Games](http://arxiv.org/abs/1501.03837) (journalArticle) - [2048 Without New Tiles Is Still Hard](http://drops.dagstuhl.de/opus/volltexte/2016/5885/) (journalArticle) +- [On the Complexity of Slide-and-Merge Games](http://arxiv.org/abs/1501.03837) (journalArticle) +- [Temporal difference learning of N-tuple networks for the game 2048](http://ieeexplore.ieee.org/document/6932907/) (conferencePaper) +- [Multi-Stage Temporal Difference Learning for 2048-like Games](http://arxiv.org/abs/1606.07374v2) (journalArticle) +- [Making Change in 2048](http://arxiv.org/abs/1804.07396v1) (journalArticle) +- [2048 is (PSPACE) Hard, but Sometimes Easy](http://arxiv.org/abs/1408.6315v1) (journalArticle) +- [Analysis of the Game "2048" and its Generalization in Higher Dimensions](http://arxiv.org/abs/1804.07393v2) (journalArticle) +- [Threes!, Fives, 1024!, and 2048 are Hard](http://arxiv.org/abs/1505.04274v1) (journalArticle) +- [An investigation into 2048 AI strategies](https://doi.org/10.1109%2Fcig.2014.6932920) (conferencePaper) +- [Systematic selection of N-tuple networks with consideration of interinfluence for game 2048](https://doi.org/10.1109%2Ftaai.2016.7880154) (conferencePaper) +- [Systematic Selection of N-Tuple Networks for 2048](https://doi.org/10.1007%2F978-3-319-50935-8_8) (bookSection) # Accessibility -- [Meeple Centred Design: A Heuristic Toolkit for Evaluating the Accessibility of Tabletop Games](http://link.springer.com/10.1007/s40869-018-0057-8) (journalArticle) - [Eighteen Months of Meeple Like Us: An Exploration into the State of Board Game Accessibility](http://link.springer.com/10.1007/s40869-018-0056-9) (journalArticle) +- [Meeple Centred Design: A Heuristic Toolkit for Evaluating the Accessibility of Tabletop Games](http://link.springer.com/10.1007/s40869-018-0057-8) (journalArticle) # Azul -- [A summary of a dissertation on Azul](https://old.reddit.com/r/boardgames/comments/hxodaf/update_i_wrote_my_dissertation_on_azul/) (report) -- [Ceramic: A research environment based on the multi-player strategic board game Azul](https://ipsj.ixsq.nii.ac.jp/ej/?action=repository_action_common_download&item_id=207669&item_no=1&attribute_id=1&file_no=1) (conferencePaper) - [Ceramic: A research environment based on the multi-player strategic board game Azul](https://github.com/Swynfel/ceramic) (computerProgram) +- [Ceramic: A research environment based on the multi-player strategic board game Azul](https://ipsj.ixsq.nii.ac.jp/ej/?action=repository_action_common_download&item_id=207669&item_no=1&attribute_id=1&file_no=1) (conferencePaper) +- [A summary of a dissertation on Azul](https://old.reddit.com/r/boardgames/comments/hxodaf/update_i_wrote_my_dissertation_on_azul/) (report) # Blokus -- [Blokus Game Solver](https://digitalcommons.calpoly.edu/cpesp/290/) (report) -- [FPGA Blokus Duo Solver using a massively parallel architecture](http://ieeexplore.ieee.org/document/6718426/) (conferencePaper) - [Blokus Duo game on FPGA](http://ieeexplore.ieee.org/document/6714256/) (conferencePaper) +- [FPGA Blokus Duo Solver using a massively parallel architecture](http://ieeexplore.ieee.org/document/6718426/) (conferencePaper) +- [Blokus Game Solver](https://digitalcommons.calpoly.edu/cpesp/290/) (report) # Carcassonne - [Playing Carcassonne with Monte Carlo Tree Search](http://arxiv.org/abs/2009.12974) (journalArticle) # Diplomacy +- [Human-Level Performance in No-Press Diplomacy via Equilibrium Search](http://arxiv.org/abs/2010.02923) (journalArticle) +- [Monte Carlo Tree Search for the Game of Diplomacy](https://dl.acm.org/doi/10.1145/3411408.3411413) (conferencePaper) +- [Agent Madoff: A Heuristic-Based Negotiation Agent For The Diplomacy Strategy Game](http://arxiv.org/abs/1902.06996v1) (journalArticle) - [Learning to Play No-Press Diplomacy with Best Response Policy Iteration](http://arxiv.org/abs/2006.04635v2) (journalArticle) - [No Press Diplomacy: Modeling Multi-Agent Gameplay](http://arxiv.org/abs/1909.02128v2) (journalArticle) -- [Agent Madoff: A Heuristic-Based Negotiation Agent For The Diplomacy Strategy Game](http://arxiv.org/abs/1902.06996v1) (journalArticle) -- [Monte Carlo Tree Search for the Game of Diplomacy](https://dl.acm.org/doi/10.1145/3411408.3411413) (conferencePaper) -- [Human-Level Performance in No-Press Diplomacy via Equilibrium Search](http://arxiv.org/abs/2010.02923) (journalArticle) # Dixit -- [Creative Captioning: An AI Grand Challenge Based on the Dixit Board Game](http://arxiv.org/abs/2010.00048) (journalArticle) - [Dixit: Interactive Visual Storytelling via Term Manipulation](http://arxiv.org/abs/1903.02230) (journalArticle) +- [Creative Captioning: An AI Grand Challenge Based on the Dixit Board Game](http://arxiv.org/abs/2010.00048) (journalArticle) # Dominion -- [Dominion Simulator](https://dominionsimulator.wordpress.com/f-a-q/) (computerProgram) -- [Dominion Simulator Source Code](https://github.com/mikemccllstr/dominionstats/) (computerProgram) -- [Best and worst openings in Dominion](http://councilroom.com/openings) (blogPost) -- [Optimal Card Ratios in Dominion](http://councilroom.com/optimal_card_ratios) (blogPost) -- [Card Winning Stats on Dominion Server](http://councilroom.com/supply_win) (blogPost) -- [Dominion Strategy Forum](http://forum.dominionstrategy.com/index.php) (forumPost) - [Clustering Player Strategies from Variable-Length Game Logs in Dominion](http://arxiv.org/abs/1811.11273) (journalArticle) +- [Dominion Strategy Forum](http://forum.dominionstrategy.com/index.php) (forumPost) +- [Card Winning Stats on Dominion Server](http://councilroom.com/supply_win) (blogPost) +- [Optimal Card Ratios in Dominion](http://councilroom.com/optimal_card_ratios) (blogPost) +- [Best and worst openings in Dominion](http://councilroom.com/openings) (blogPost) +- [Dominion Simulator Source Code](https://github.com/mikemccllstr/dominionstats/) (computerProgram) +- [Dominion Simulator](https://dominionsimulator.wordpress.com/f-a-q/) (computerProgram) # Frameworks -- [RLCard: A Toolkit for Reinforcement Learning in Card Games](http://arxiv.org/abs/1910.04376) (journalArticle) -- [Design and Implementation of TAG: A Tabletop Games Framework](http://arxiv.org/abs/2009.12065) (journalArticle) -- [Game Tree Search Algorithms - C++ library for AI bot programming.](https://github.com/AdamStelmaszczyk/gtsa) (computerProgram) - [TAG: Tabletop Games Framework](https://github.com/GAIGResearch/TabletopGames) (computerProgram) +- [Game Tree Search Algorithms - C++ library for AI bot programming.](https://github.com/AdamStelmaszczyk/gtsa) (computerProgram) +- [Design and Implementation of TAG: A Tabletop Games Framework](http://arxiv.org/abs/2009.12065) (journalArticle) +- [RLCard: A Toolkit for Reinforcement Learning in Card Games](http://arxiv.org/abs/1910.04376) (journalArticle) # Game Design -- [MDA: A Formal Approach to Game Design and Game Research](https://aaai.org/Library/Workshops/2004/ws04-04-001.php) (conferencePaper) - [Exploring anonymity in cooperative board games](http://www.digra.org/digital-library/publications/exploring-anonymity-in-cooperative-board-games/) (conferencePaper) +- [MDA: A Formal Approach to Game Design and Game Research](https://aaai.org/Library/Workshops/2004/ws04-04-001.php) (conferencePaper) + +# General Gameplay +- [Player of Games](http://arxiv.org/abs/2112.03178) (journalArticle) +- [Mastering the game of Go without human knowledge](http://www.nature.com/articles/nature24270) (journalArticle) +- [AlphaZero for a Non-Deterministic Game](https://ieeexplore.ieee.org/document/8588490/) (conferencePaper) +- [A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play](https://www.science.org/doi/10.1126/science.aar6404) (journalArticle) # Hanabi -- [How to Make the Perfect Fireworks Display: Two Strategies forHanabi](https://doi.org/10.4169%2Fmath.mag.88.5.323) (journalArticle) -- [Evaluating and modelling Hanabi-playing agents](https://doi.org/10.1109%2Fcec.2017.7969465) (conferencePaper) -- [The Hanabi challenge: A new frontier for AI research](https://doi.org/10.1016%2Fj.artint.2019.103216) (journalArticle) -- [The 2018 Hanabi competition](https://doi.org/10.1109%2Fcig.2019.8848008) (conferencePaper) - [Diverse Agents for Ad-Hoc Cooperation in Hanabi](https://doi.org/10.1109%2Fcig.2019.8847944) (conferencePaper) -- [Improving Policies via Search in Cooperative Partially Observable Games](http://arxiv.org/abs/1912.02318v1) (journalArticle) -- [Hanabi is NP-hard, Even for Cheaters who Look at Their Cards](http://arxiv.org/abs/1603.01911v3) (journalArticle) -- [Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi](http://arxiv.org/abs/2004.13710v2) (journalArticle) -- [Evaluating the Rainbow DQN Agent in Hanabi with Unseen Partners](http://arxiv.org/abs/2004.13291v1) (journalArticle) -- [Re-determinizing MCTS in Hanabi]() (conferencePaper) -- [Evolving Agents for the Hanabi 2018 CIG Competition](https://ieeexplore.ieee.org/document/8490449/) (conferencePaper) -- [Aspects of the Cooperative Card Game Hanabi](http://link.springer.com/10.1007/978-3-319-67468-1_7) (bookSection) -- [Playing Hanabi Near-Optimally](http://link.springer.com/10.1007/978-3-319-71649-7_5) (bookSection) -- [An intentional AI for hanabi](http://ieeexplore.ieee.org/document/8080417/) (conferencePaper) -- [Solving Hanabi: Estimating Hands by Opponent's Actions in Cooperative Game with Incomplete Information](https://aaai.org/ocs/index.php/WS/AAAIW15/paper/view/10167) (conferencePaper) -- [A Browser-based Interface for the Exploration and Evaluation of Hanabi AIs](http://fdg2017.org/papers/FDG2017_demo_Hanabi.pdf) (journalArticle) -- [I see what you see: Integrating eye tracking into Hanabi playing agents]() (journalArticle) -- [State of the art Hanabi bots + simulation framework in rust](https://github.com/WuTheFWasThat/hanabi.rs) (computerProgram) -- [A strategy simulator for the well-known cooperative card game Hanabi](https://github.com/rjtobin/HanSim) (computerProgram) -- [A framework for writing bots that play Hanabi](https://github.com/Quuxplusone/Hanabi) (computerProgram) -- [Operationalizing Intentionality to Play Hanabi with Human Players](https://ieeexplore.ieee.org/document/9140404/) (journalArticle) -- [Behavioral Evaluation of Hanabi Rainbow DQN Agents and Rule-Based Agents](https://ojs.aaai.org/index.php/AIIDE/article/view/7404) (journalArticle) -- [Playing mini-Hanabi card game with Q-learning](http://id.nii.ac.jp/1001/00205046/) (conferencePaper) -- [Hanabi Open Agent Dataset](https://github.com/aronsar/hoad) (computerProgram) -- [Hanabi Open Agent Dataset](https://dl.acm.org/doi/10.5555/3463952.3464188) (conferencePaper) -- [Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi](http://arxiv.org/abs/2107.07630) (journalArticle) -- [A Graphical User Interface For The Hanabi Challenge Benchmark](http://oru.diva-portal.org/smash/record.jsf?pid=diva2%3A1597503) (thesis) +- [The 2018 Hanabi competition](https://doi.org/10.1109%2Fcig.2019.8848008) (conferencePaper) +- [Evaluating and modelling Hanabi-playing agents](https://doi.org/10.1109%2Fcec.2017.7969465) (conferencePaper) +- [How to Make the Perfect Fireworks Display: Two Strategies forHanabi](https://doi.org/10.4169%2Fmath.mag.88.5.323) (journalArticle) - [Emergence of Cooperative Impression With Self-Estimation, Thinking Time, and Concordance of Risk Sensitivity in Playing Hanabi](https://www.frontiersin.org/articles/10.3389/frobt.2021.658348/full) (journalArticle) +- [A Graphical User Interface For The Hanabi Challenge Benchmark](http://oru.diva-portal.org/smash/record.jsf?pid=diva2%3A1597503) (thesis) +- [Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi](http://arxiv.org/abs/2107.07630) (journalArticle) +- [Hanabi Open Agent Dataset](https://dl.acm.org/doi/10.5555/3463952.3464188) (conferencePaper) +- [Hanabi Open Agent Dataset](https://github.com/aronsar/hoad) (computerProgram) +- [Playing mini-Hanabi card game with Q-learning](http://id.nii.ac.jp/1001/00205046/) (conferencePaper) +- [Behavioral Evaluation of Hanabi Rainbow DQN Agents and Rule-Based Agents](https://ojs.aaai.org/index.php/AIIDE/article/view/7404) (journalArticle) +- [Operationalizing Intentionality to Play Hanabi with Human Players](https://ieeexplore.ieee.org/document/9140404/) (journalArticle) +- [A framework for writing bots that play Hanabi](https://github.com/Quuxplusone/Hanabi) (computerProgram) +- [A strategy simulator for the well-known cooperative card game Hanabi](https://github.com/rjtobin/HanSim) (computerProgram) +- [State of the art Hanabi bots + simulation framework in rust](https://github.com/WuTheFWasThat/hanabi.rs) (computerProgram) +- [I see what you see: Integrating eye tracking into Hanabi playing agents]() (journalArticle) +- [A Browser-based Interface for the Exploration and Evaluation of Hanabi AIs](http://fdg2017.org/papers/FDG2017_demo_Hanabi.pdf) (journalArticle) +- [Solving Hanabi: Estimating Hands by Opponent's Actions in Cooperative Game with Incomplete Information](https://aaai.org/ocs/index.php/WS/AAAIW15/paper/view/10167) (conferencePaper) +- [An intentional AI for hanabi](http://ieeexplore.ieee.org/document/8080417/) (conferencePaper) +- [Playing Hanabi Near-Optimally](http://link.springer.com/10.1007/978-3-319-71649-7_5) (bookSection) +- [Aspects of the Cooperative Card Game Hanabi](http://link.springer.com/10.1007/978-3-319-67468-1_7) (bookSection) +- [Evolving Agents for the Hanabi 2018 CIG Competition](https://ieeexplore.ieee.org/document/8490449/) (conferencePaper) +- [Re-determinizing MCTS in Hanabi]() (conferencePaper) +- [The Hanabi challenge: A new frontier for AI research](https://doi.org/10.1016%2Fj.artint.2019.103216) (journalArticle) +- [Evaluating the Rainbow DQN Agent in Hanabi with Unseen Partners](http://arxiv.org/abs/2004.13291v1) (journalArticle) +- [Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi](http://arxiv.org/abs/2004.13710v2) (journalArticle) +- [Hanabi is NP-hard, Even for Cheaters who Look at Their Cards](http://arxiv.org/abs/1603.01911v3) (journalArticle) +- [Improving Policies via Search in Cooperative Partially Observable Games](http://arxiv.org/abs/1912.02318v1) (journalArticle) # Hive - [On the complexity of Hive](https://dspace.library.uu.nl/handle/1874/396955) (thesis) # Jenga -- [Jidoukan Jenga: Teaching English through remixing games and game rules](https://www.llpjournal.org/2020/04/13/jidokan-jenga.html) (journalArticle) - [Maximum genus of the Jenga like configurations](http://arxiv.org/abs/1708.01503) (journalArticle) +- [Jidoukan Jenga: Teaching English through remixing games and game rules](https://www.llpjournal.org/2020/04/13/jidokan-jenga.html) (journalArticle) # Kingdomino - [Monte Carlo Methods for the Game Kingdomino](https://doi.org/10.1109%2Fcig.2018.8490419) (conferencePaper) @@ -171,26 +178,26 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub]( - [Applying Neural Networks and Genetic Programming to the Game Lost Cities](https://minds.wisconsin.edu/bitstream/handle/1793/79080/LydeenSpr18.pdf?sequence=1&isAllowed=y) (conferencePaper) # Mafia -- [A mathematical model of the Mafia game](http://arxiv.org/abs/1009.1031v3) (journalArticle) -- [Automatic Long-Term Deception Detection in Group Interaction Videos](http://arxiv.org/abs/1905.08617) (journalArticle) -- [Human-Side Strategies in the Werewolf Game Against the Stealth Werewolf Strategy](http://link.springer.com/10.1007/978-3-319-50935-8_9) (bookSection) - [A Theoretical Study of Mafia Games](http://arxiv.org/abs/0804.0071) (journalArticle) +- [Human-Side Strategies in the Werewolf Game Against the Stealth Werewolf Strategy](http://link.springer.com/10.1007/978-3-319-50935-8_9) (bookSection) +- [Automatic Long-Term Deception Detection in Group Interaction Videos](http://arxiv.org/abs/1905.08617) (journalArticle) +- [A mathematical model of the Mafia game](http://arxiv.org/abs/1009.1031v3) (journalArticle) # Magic: The Gathering - [Ensemble Determinization in Monte Carlo Tree Search for the Imperfect Information Card Game Magic: The Gathering](https://doi.org/10.1109%2Ftciaig.2012.2204883) (journalArticle) -- [Optimal Card-Collecting Strategies for Magic: The Gathering](https://doi.org/10.1080%2F07468342.2000.11974103) (journalArticle) - [Monte Carlo search applied to card selection in Magic: The Gathering](https://doi.org/10.1109%2Fcig.2009.5286501) (conferencePaper) -- [Magic: the Gathering is as Hard as Arithmetic](http://arxiv.org/abs/2003.05119v1) (journalArticle) -- [Magic: The Gathering is Turing Complete](http://arxiv.org/abs/1904.09828v2) (journalArticle) -- [Neural Networks Models for Analyzing Magic: the Gathering Cards](http://arxiv.org/abs/1810.03744v1) (journalArticle) -- [Neural Networks Models for Analyzing Magic: The Gathering Cards](http://link.springer.com/10.1007/978-3-030-04179-3_20) (bookSection) -- [The Complexity of Deciding Legality of a Single Step of Magic: The Gathering](https://livrepository.liverpool.ac.uk/3029568/) (conferencePaper) -- [Magic: The Gathering in Common Lisp](https://vixra.org/abs/2001.0065) (conferencePaper) -- [Magic: The Gathering in Common Lisp](https://github.com/jeffythedragonslayer/maglisp) (computerProgram) -- [Mathematical programming and Magic: The Gathering](https://commons.lib.niu.edu/handle/10843/19194) (thesis) -- [Deck Construction Strategies for Magic: The Gathering](https://www.doi.org/10.1685/CSC06077) (conferencePaper) -- [Deckbuilding in Magic: The Gathering Using a Genetic Algorithm](https://doi.org/11250/2462429) (thesis) +- [Optimal Card-Collecting Strategies for Magic: The Gathering](https://doi.org/10.1080%2F07468342.2000.11974103) (journalArticle) - [Magic: The Gathering Deck Performance Prediction](http://cs229.stanford.edu/proj2012/HauPlotkinTran-MagicTheGatheringDeckPerformancePrediction.pdf) (report) +- [Deckbuilding in Magic: The Gathering Using a Genetic Algorithm](https://doi.org/11250/2462429) (thesis) +- [Deck Construction Strategies for Magic: The Gathering](https://www.doi.org/10.1685/CSC06077) (conferencePaper) +- [Mathematical programming and Magic: The Gathering](https://commons.lib.niu.edu/handle/10843/19194) (thesis) +- [Magic: The Gathering in Common Lisp](https://github.com/jeffythedragonslayer/maglisp) (computerProgram) +- [Magic: The Gathering in Common Lisp](https://vixra.org/abs/2001.0065) (conferencePaper) +- [The Complexity of Deciding Legality of a Single Step of Magic: The Gathering](https://livrepository.liverpool.ac.uk/3029568/) (conferencePaper) +- [Neural Networks Models for Analyzing Magic: The Gathering Cards](http://link.springer.com/10.1007/978-3-030-04179-3_20) (bookSection) +- [Neural Networks Models for Analyzing Magic: the Gathering Cards](http://arxiv.org/abs/1810.03744v1) (journalArticle) +- [Magic: The Gathering is Turing Complete](http://arxiv.org/abs/1904.09828v2) (journalArticle) +- [Magic: the Gathering is as Hard as Arithmetic](http://arxiv.org/abs/2003.05119v1) (journalArticle) # Mobile Games - [Trainyard is NP-Hard](http://arxiv.org/abs/1603.00928v1) (journalArticle) @@ -201,30 +208,30 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub]( # Monopoly - [Monopoly as a Markov Process](https://doi.org/10.1080%2F0025570x.1972.11976187) (journalArticle) -- [Learning Monopoly Gameplay: A Hybrid Model-Free Deep Reinforcement Learning and Imitation Learning Approach](http://arxiv.org/abs/2103.00683) (journalArticle) -- [Negotiation strategy of agents in the MONOPOLY game](http://ieeexplore.ieee.org/document/1013210/) (conferencePaper) -- [Generating interesting Monopoly boards from open data](http://ieeexplore.ieee.org/document/6374168/) (conferencePaper) -- [Estimating the probability that the game of Monopoly never ends](http://ieeexplore.ieee.org/document/5429349/) (conferencePaper) -- [Learning to Play Monopoly with Monte Carlo Tree Search](https://project-archive.inf.ed.ac.uk/ug4/20181042/ug4_proj.pdf) (report) -- [Monopoly Using Reinforcement Learning](https://ieeexplore.ieee.org/document/8929523/) (conferencePaper) -- [A Markovian Exploration of Monopoly](https://pi4.math.illinois.edu/wp-content/uploads/2014/10/Gartland-Burson-Ferguson-Markovopoly.pdf) (report) -- [Learning to play Monopoly: A Reinforcement Learning approach](https://intelligence.csd.auth.gr/publication/conference-papers/learning-to-play-monopoly-a-reinforcement-learning-approach/) (conferencePaper) - [What’s the Best Monopoly Strategy?](https://core.ac.uk/download/pdf/48614184.pdf) (presentation) +- [Learning to play Monopoly: A Reinforcement Learning approach](https://intelligence.csd.auth.gr/publication/conference-papers/learning-to-play-monopoly-a-reinforcement-learning-approach/) (conferencePaper) +- [A Markovian Exploration of Monopoly](https://pi4.math.illinois.edu/wp-content/uploads/2014/10/Gartland-Burson-Ferguson-Markovopoly.pdf) (report) +- [Monopoly Using Reinforcement Learning](https://ieeexplore.ieee.org/document/8929523/) (conferencePaper) +- [Learning to Play Monopoly with Monte Carlo Tree Search](https://project-archive.inf.ed.ac.uk/ug4/20181042/ug4_proj.pdf) (report) +- [Estimating the probability that the game of Monopoly never ends](http://ieeexplore.ieee.org/document/5429349/) (conferencePaper) +- [Generating interesting Monopoly boards from open data](http://ieeexplore.ieee.org/document/6374168/) (conferencePaper) +- [Negotiation strategy of agents in the MONOPOLY game](http://ieeexplore.ieee.org/document/1013210/) (conferencePaper) +- [Learning Monopoly Gameplay: A Hybrid Model-Free Deep Reinforcement Learning and Imitation Learning Approach](http://arxiv.org/abs/2103.00683) (journalArticle) # Monopoly Deal - [Implementation of Artificial Intelligence with 3 Different Characters of AI Player on “Monopoly Deal” Computer Game](https://doi.org/10.1007%2F978-3-662-46742-8_11) (bookSection) # Nmbr9 -- [Nmbr9 as a Constraint Programming Challenge](http://arxiv.org/abs/2001.04238) (journalArticle) - [Nmbr9 as a Constraint Programming Challenge](https://zayenz.se/blog/post/nmbr9-cp2019-abstract/) (blogPost) +- [Nmbr9 as a Constraint Programming Challenge](http://arxiv.org/abs/2001.04238) (journalArticle) # Pandemic - [NP-Completeness of Pandemic](https://www.jstage.jst.go.jp/article/ipsjjip/20/3/20_723/_article) (journalArticle) # Patchwork -- [State Representation and Polyomino Placement for the Game Patchwork](https://zayenz.se/blog/post/patchwork-modref2019-paper/) (blogPost) -- [State Representation and Polyomino Placement for the Game Patchwork](http://arxiv.org/abs/2001.04233) (journalArticle) - [State Representation and Polyomino Placement for the Game Patchwork](https://zayenz.se/papers/Lagerkvist_ModRef_2019_Presentation.pdf) (presentation) +- [State Representation and Polyomino Placement for the Game Patchwork](http://arxiv.org/abs/2001.04233) (journalArticle) +- [State Representation and Polyomino Placement for the Game Patchwork](https://zayenz.se/blog/post/patchwork-modref2019-paper/) (blogPost) # Pentago - [On Solving Pentago](http://www.ke.tu-darmstadt.de/lehre/arbeiten/bachelor/2011/Buescher_Niklas.pdf) (thesis) @@ -240,21 +247,21 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub]( - [Finding Friend and Foe in Multi-Agent Games](http://arxiv.org/abs/1906.02330) (journalArticle) # RISK -- [Mini-Risk: Strategies for a Simplified Board Game](https://doi.org/10.1057%2Fjors.1990.2) (journalArticle) -- [Learning the risk board game with classifier systems](https://doi.org/10.1145%2F508791.508904) (conferencePaper) -- [Markov Chains and the RISK Board Game](https://doi.org/10.1080%2F0025570x.1997.11996573) (journalArticle) -- [Markov Chains for the RISK Board Game Revisited](https://doi.org/10.1080%2F0025570x.2003.11953165) (journalArticle) - [Planning an Endgame Move Set for the Game RISK: A Comparison of Search Algorithms](https://doi.org/10.1109%2Ftevc.2005.856211) (journalArticle) -- [An Intelligent Artificial Player for the Game of Risk](http://www.ke.tu-darmstadt.de/lehre/archiv/ss04/oberseminar/folien/Wolf_Michael-Slides.pdf) (presentation) -- [RISKy Business: An In-Depth Look at the Game RISK](https://scholar.rose-hulman.edu/rhumj/vol3/iss2/3) (journalArticle) -- [RISK Board Game ‐ Battle Outcome Analysis](http://www.c4i.gr/xgeorgio/docs/RISK-board-game%20_rev-3.pdf) (journalArticle) -- [A multi-agent system for playing the board game risk](http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3781) (thesis) -- [Monte Carlo Tree Search for Risk](https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-SAS-OCS-ORA-2020/MP-SAS-OCS-ORA-2020-WCM-01.pdf) (conferencePaper) +- [Markov Chains and the RISK Board Game](https://doi.org/10.1080%2F0025570x.1997.11996573) (journalArticle) +- [Learning the risk board game with classifier systems](https://doi.org/10.1145%2F508791.508904) (conferencePaper) +- [Mini-Risk: Strategies for a Simplified Board Game](https://doi.org/10.1057%2Fjors.1990.2) (journalArticle) +- [Markov Chains for the RISK Board Game Revisited](https://doi.org/10.1080%2F0025570x.2003.11953165) (journalArticle) - [Wargaming with Monte-Carlo Tree Search](https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-SAS-OCS-ORA-2020/MP-SAS-OCS-ORA-2020-WCM-01P.pdf) (presentation) +- [Monte Carlo Tree Search for Risk](https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-SAS-OCS-ORA-2020/MP-SAS-OCS-ORA-2020-WCM-01.pdf) (conferencePaper) +- [A multi-agent system for playing the board game risk](http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3781) (thesis) +- [An Intelligent Artificial Player for the Game of Risk](http://www.ke.tu-darmstadt.de/lehre/archiv/ss04/oberseminar/folien/Wolf_Michael-Slides.pdf) (presentation) +- [RISK Board Game ‐ Battle Outcome Analysis](http://www.c4i.gr/xgeorgio/docs/RISK-board-game%20_rev-3.pdf) (journalArticle) +- [RISKy Business: An In-Depth Look at the Game RISK](https://scholar.rose-hulman.edu/rhumj/vol3/iss2/3) (journalArticle) # Santorini -- [A Mathematical Analysis of the Game of Santorini](https://openworks.wooster.edu/independentstudy/8917/) (thesis) - [A Mathematical Analysis of the Game of Santorini](https://github.com/carsongeissler/SantoriniIS) (computerProgram) +- [A Mathematical Analysis of the Game of Santorini](https://openworks.wooster.edu/independentstudy/8917/) (thesis) # Scotland Yard - [The complexity of Scotland Yard](https://eprints.illc.uva.nl/id/eprint/193/1/PP-2006-18.text.pdf) (report) @@ -263,28 +270,28 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub]( - [Competing in a Complex Hidden Role Game with Information Set Monte Carlo Tree Search](http://arxiv.org/abs/2005.07156) (journalArticle) # Set -- [Game, Set, Math](https://doi.org/10.4169%2Fmath.mag.85.2.083) (journalArticle) - [The Joy of SET](https://doi.org/10.1080%2F00029890.2018.1412661) (journalArticle) +- [Game, Set, Math](https://doi.org/10.4169%2Fmath.mag.85.2.083) (journalArticle) # Settlers of Catan -- [The effectiveness of persuasion in The Settlers of Catan](https://doi.org/10.1109%2Fcig.2014.6932861) (conferencePaper) -- [Avoiding Revenge Using Optimal Opponent Ranking Strategy in the Board Game Catan](https://doi.org/10.4018%2Fijgcms.2018040103) (journalArticle) - [Game strategies for The Settlers of Catan](https://doi.org/10.1109%2Fcig.2014.6932884) (conferencePaper) -- [Monte-Carlo Tree Search in Settlers of Catan](https://doi.org/10.1007%2F978-3-642-12993-3_3) (bookSection) - [Deep Reinforcement Learning in Strategic Board Game Environments](https://doi.org/10.1007%2F978-3-030-14174-5_16) (bookSection) -- [Settlers of Catan bot trained using reinforcement learning](https://jonzia.github.io/Catan/) (computerProgram) -- [Trading in a multiplayer board game: Towards an analysis of non-cooperative dialogue](https://escholarship.org/uc/item/9zt506xx) (conferencePaper) -- [POMCP with Human Preferencesin Settlers of Catan](https://www.aaai.org/ocs/index.php/AIIDE/AIIDE18/paper/viewFile/18091/17217) (journalArticle) -- [The impact of loaded dice in Catan](https://izbicki.me/blog/how-to-cheat-at-settlers-of-catan-by-loading-the-dice-and-prove-it-with-p-values.html) (blogPost) -- [Monte Carlo Tree Search in a Modern Board Game Framework](https://project.dke.maastrichtuniversity.nl/games/files/bsc/Roelofs_Bsc-paper.pdf) (journalArticle) +- [Monte-Carlo Tree Search in Settlers of Catan](https://doi.org/10.1007%2F978-3-642-12993-3_3) (bookSection) +- [Avoiding Revenge Using Optimal Opponent Ranking Strategy in the Board Game Catan](https://doi.org/10.4018%2Fijgcms.2018040103) (journalArticle) +- [The effectiveness of persuasion in The Settlers of Catan](https://doi.org/10.1109%2Fcig.2014.6932861) (conferencePaper) +- [Strategic Dialogue Management via Deep Reinforcement Learning](http://arxiv.org/abs/1511.08099) (journalArticle) - [Reinforcement Learning of Strategies for Settlers of Catan](https://www.researchgate.net/publication/228728063_Reinforcement_learning_of_strategies_for_Settlers_of_Catan) (conferencePaper) +- [Trading in a multiplayer board game: Towards an analysis of non-cooperative dialogue](https://escholarship.org/uc/item/9zt506xx) (conferencePaper) +- [Strategic Dialogue Management via Deep Reinforcement Learning](http://arxiv.org/abs/1511.08099) (journalArticle) - [Playing Catan with Cross-dimensional Neural Network](http://arxiv.org/abs/2008.07079) (journalArticle) -- [Strategic Dialogue Management via Deep Reinforcement Learning](http://arxiv.org/abs/1511.08099) (journalArticle) -- [Strategic Dialogue Management via Deep Reinforcement Learning](http://arxiv.org/abs/1511.08099) (journalArticle) +- [Monte Carlo Tree Search in a Modern Board Game Framework](https://project.dke.maastrichtuniversity.nl/games/files/bsc/Roelofs_Bsc-paper.pdf) (journalArticle) +- [The impact of loaded dice in Catan](https://izbicki.me/blog/how-to-cheat-at-settlers-of-catan-by-loading-the-dice-and-prove-it-with-p-values.html) (blogPost) +- [POMCP with Human Preferencesin Settlers of Catan](https://www.aaai.org/ocs/index.php/AIIDE/AIIDE18/paper/viewFile/18091/17217) (journalArticle) +- [Settlers of Catan bot trained using reinforcement learning](https://jonzia.github.io/Catan/) (computerProgram) # Shobu -- [Shobu AI Playground](https://github.com/JayWalker512/Shobu) (computerProgram) - [Shobu randomly played games dataset](https://www.kaggle.com/bsfoltz/shobu-randomly-played-games-104k) (webpage) +- [Shobu AI Playground](https://github.com/JayWalker512/Shobu) (computerProgram) # Terra Mystica - [Using Tabu Search Algorithm for Map Generation in the Terra Mystica Tabletop Game](https://doi.org/10.1145%2F3396474.3396492) (conferencePaper) @@ -293,11 +300,11 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub]( - [A New Challenge: Approaching Tetris Link with AI](http://arxiv.org/abs/2004.00377) (journalArticle) # Ticket to Ride -- [AI-based playtesting of contemporary board games](http://dl.acm.org/citation.cfm?doid=3102071.3102105) (conferencePaper) -- [Materials for Ticket to Ride Seattle and a framework for making more game boards](https://github.com/dovinmu/ttr_generator) (computerProgram) - [The Difficulty of Learning Ticket to Ride](https://www.eecs.tufts.edu/~jsinapov/teaching/comp150_RL/reports/Nguyen_Dinjian_report.pdf) (report) -- [Evolving maps and decks for ticket to ride](https://dl.acm.org/doi/10.1145/3235765.3235813) (conferencePaper) - [Applications of Graph Theory and Probability in the Board Game Ticket to Ride](https://www.rtealwitter.com/slides/2020-JMM.pdf) (presentation) +- [Evolving maps and decks for ticket to ride](https://dl.acm.org/doi/10.1145/3235765.3235813) (conferencePaper) +- [Materials for Ticket to Ride Seattle and a framework for making more game boards](https://github.com/dovinmu/ttr_generator) (computerProgram) +- [AI-based playtesting of contemporary board games](http://dl.acm.org/citation.cfm?doid=3102071.3102105) (conferencePaper) # Ultimate Tic-Tac-Toe - [At Most 43 Moves, At Least 29: Optimal Strategies and Bounds for Ultimate Tic-Tac-Toe](http://arxiv.org/abs/2006.02353) (journalArticle) @@ -308,16 +315,16 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub]( # Yahtzee - [Nearly Optimal Computer Play in Multi-player Yahtzee](https://doi.org/10.1007%2F978-3-642-17928-0_23) (bookSection) -- [Computer Strategies for Solitaire Yahtzee](https://doi.org/10.1109%2Fcig.2007.368089) (conferencePaper) - [Modeling expert problem solving in a game of chance: a Yahtzeec case study](https://doi.org/10.1111%2F1468-0394.00160) (journalArticle) -- [Probabilites In Yahtzee](https://pubs.nctm.org/view/journals/mt/75/9/article-p751.xml) (journalArticle) -- [Optimal Solitaire Yahtzee Strategies](http://www.yahtzee.org.uk/optimal_yahtzee_TV.pdf) (presentation) -- [Yahtzee: a Large Stochastic Environment for RL Benchmarks](http://researchers.lille.inria.fr/~lazaric/Webpage/PublicationsByTopic_files/bonarini2005yahtzee.pdf) (journalArticle) -- [Optimal Yahtzee performance in multi-player games](https://www.csc.kth.se/utbildning/kth/kurser/DD143X/dkand13/Group4Per/report/12-serra-widell-nigata.pdf) (thesis) -- [How to Maximize Your Score in Solitaire Yahtzee](http://www-set.win.tue.nl/~wstomv/misc/yahtzee/yahtzee-report-unfinished.pdf) (manuscript) -- [Using Deep Q-Learning to Compare Strategy Ladders of Yahtzee](https://raw.githubusercontent.com/philvasseur/Yahtzee-DQN-Thesis/dcf2bfe15c3b8c0ff3256f02dd3c0aabdbcbc9bb/webpage/final_report.pdf) (thesis) -- [Defensive Yahtzee](http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168668) (report) - [An Optimal Strategy for Yahtzee](http://www.cs.loyola.edu/~jglenn/research/optimal_yahtzee.pdf) (report) +- [Defensive Yahtzee](http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168668) (report) +- [Using Deep Q-Learning to Compare Strategy Ladders of Yahtzee](https://raw.githubusercontent.com/philvasseur/Yahtzee-DQN-Thesis/dcf2bfe15c3b8c0ff3256f02dd3c0aabdbcbc9bb/webpage/final_report.pdf) (thesis) +- [How to Maximize Your Score in Solitaire Yahtzee](http://www-set.win.tue.nl/~wstomv/misc/yahtzee/yahtzee-report-unfinished.pdf) (manuscript) +- [Optimal Yahtzee performance in multi-player games](https://www.csc.kth.se/utbildning/kth/kurser/DD143X/dkand13/Group4Per/report/12-serra-widell-nigata.pdf) (thesis) +- [Yahtzee: a Large Stochastic Environment for RL Benchmarks](http://researchers.lille.inria.fr/~lazaric/Webpage/PublicationsByTopic_files/bonarini2005yahtzee.pdf) (journalArticle) +- [Optimal Solitaire Yahtzee Strategies](http://www.yahtzee.org.uk/optimal_yahtzee_TV.pdf) (presentation) +- [Probabilites In Yahtzee](https://pubs.nctm.org/view/journals/mt/75/9/article-p751.xml) (journalArticle) +- [Computer Strategies for Solitaire Yahtzee](https://doi.org/10.1109%2Fcig.2007.368089) (conferencePaper) # Similar Projects - https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers diff --git a/boardgame-research.rdf b/boardgame-research.rdf index ee94124..edbd6b7 100644 --- a/boardgame-research.rdf +++ b/boardgame-research.rdf @@ -5,64 +5,133 @@ xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:bib="http://purl.org/net/biblio#" xmlns:foaf="http://xmlns.com/foaf/0.1/" - xmlns:link="http://purl.org/rss/1.0/modules/link/" xmlns:prism="http://prismstandard.org/namespaces/1.2/basic/" + xmlns:link="http://purl.org/rss/1.0/modules/link/" xmlns:vcard="http://nwalsh.com/rdf/vCard#"> - - conferencePaper + + bookSection - - 2014 IEEE Conference on Computational Intelligence and Games - DOI 10.1109/cig.2014.6932861 - + Computers and Games - IEEE + + Springer Berlin Heidelberg + - Guhe - Markus - - - - - Lascarides - Alex + Pawlewicz + Jakub - - The effectiveness of persuasion in The Settlers of Catan - August 2014 + Nearly Optimal Computer Play in Multi-player Yahtzee + 2011 - https://doi.org/10.1109%2Fcig.2014.6932861 + https://doi.org/10.1007%2F978-3-642-17928-0_23 - - - attachment - Submitted Version + DOI: 10.1007/978-3-642-17928-0_23 + 250–262 + + + bookSection + + + Communications in Computer and Information Science + + + + + Springer Berlin Heidelberg + + + + + + + Lazarusli + Irene A. + + + + + Lukas + Samuel + + + + + Widjaja + Patrick + + + + + Implementation of Artificial Intelligence with 3 Different Characters of AI Player on “Monopoly Deal” Computer Game + 2015 - https://www.pure.ed.ac.uk/ws/files/19353900/CIG2014.pdf + https://doi.org/10.1007%2F978-3-662-46742-8_11 - 2020-07-20 18:34:57 - 1 - application/pdf - - + DOI: 10.1007/978-3-662-46742-8_11 + 119–127 + + journalArticle - 10 - International Journal of Gaming and Computer-Mediated Simulations - DOI 10.4018/ijgcms.2018040103 + 125 + The American Mathematical Monthly + DOI 10.1080/00029890.2018.1412661 + 3 + + + + + + + Glass + Darren + + + + + + The Joy of SET + February 2018 + + + https://doi.org/10.1080%2F00029890.2018.1412661 + + + Publisher: Informa UK Limited + 284–288 + + + attachment + Full Text + + + https://twin.sci-hub.se/6684/b949dbda2e3438aae344825abb7d0ff3/glass2018.pdf#view=FitH + + + 2020-07-20 18:35:34 + 1 + application/pdf + + + journalArticle + + + 85 + Mathematics Magazine + DOI 10.4169/math.mag.85.2.083 2 @@ -70,41 +139,278 @@ - Boda - Márton Attila + Coleman + Ben + + + + + Hartshorn + Kevin - - Avoiding Revenge Using Optimal Opponent Ranking Strategy in the Board Game Catan - April 2018 + + Game, Set, Math + April 2012 - https://doi.org/10.4018%2Fijgcms.2018040103 + https://doi.org/10.4169%2Fmath.mag.85.2.083 - Publisher: IGI Global - 47–70 + Publisher: Informa UK Limited + 83–96 - + attachment Full Text - https://sci-hub.se/downloads/2020-05-28/3d/boda2018.pdf#view=FitH + https://dacemirror.sci-hub.se/journal-article/768dabc67f6adcaa34a4c087b56b4283/game-set-math-2012.pdf#view=FitH - 2020-07-20 18:22:11 + 2020-07-20 18:24:32 1 application/pdf - + + journalArticle + + + 18 + Expert Systems + DOI 10.1111/1468-0394.00160 + 2 + + + + + + + Maynard + Ken + + + + + Moss + Patrick + + + + + Whitehead + Marcus + + + + + Narayanan + S. + + + + + Garay + Matt + + + + + Brannon + Nathan + + + + + Kantamneni + Raj Gopal + + + + + Kustra + Todd + + + + + + Modeling expert problem solving in a game of chance: a Yahtzeec case study + May 2001 + + + https://doi.org/10.1111%2F1468-0394.00160 + + + Publisher: Wiley + 88–98 + + + attachment + Full Text + + + https://cyber.sci-hub.se/MTAuMTExMS8xNDY4LTAzOTQuMDAxNjA=/maynard2001.pdf#view=FitH + + + 2020-07-20 18:29:00 + 1 + application/pdf + + + bookSection + + + Case-Based Reasoning Research and Development + + + + + Springer International Publishing + + + + + + + Woolford + Michael + + + + + Watson + Ian + + + + + + SCOUT: A Case-Based Reasoning Agent for Playing Race for the Galaxy + 2017 + + + https://doi.org/10.1007%2F978-3-319-61030-6_27 + + + DOI: 10.1007/978-3-319-61030-6_27 + 390–402 + + + attachment + Woolford and Watson - 2017 - SCOUT A Case-Based Reasoning Agent for Playing Ra.pdf + application/pdf + + + journalArticle + + + 4 + IEEE Trans. Comput. Intell. AI Games + DOI 10.1109/tciaig.2012.2204883 + 4 + + + + + + + Cowling + Peter I. + + + + + Ward + Colin D. + + + + + Powley + Edward J. + + + + + + Ensemble Determinization in Monte Carlo Tree Search for the Imperfect Information Card Game Magic: The Gathering + December 2012 + + + https://doi.org/10.1109%2Ftciaig.2012.2204883 + + + Publisher: Institute of Electrical and Electronics Engineers (IEEE) + 241–257 + + + attachment + Accepted Version + + + https://eprints.whiterose.ac.uk/75050/1/EnsDetMagic.pdf + + + 2020-07-20 18:14:45 + 1 + application/pdf + + + journalArticle + + + Information Processing Letters + DOI 10.1016/j.ipl.2020.105995 + + + + + + + Mishiba + Shohei + + + + + Takenaga + Yasuhiko + + + + + + QUIXO is EXPTIME-complete + July 2020 + + + https://doi.org/10.1016%2Fj.ipl.2020.105995 + + + Publisher: Elsevier BV + 105995 + + + attachment + Full Text + + + https://pdf.sciencedirectassets.com/271527/AIP/1-s2.0-S002001902030082X/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEHoaCXVzLWVhc3QtMSJGMEQCIFtkSBpgjQCGD2t9HhDGKXByeuMLxjq5SpZiHiVJRtD2AiBGNfyOHc5rhR9YOWBJqfm4Q4sk9A7DiAYQK4bE21l10yq0AwgyEAMaDDA1OTAwMzU0Njg2NSIMTbV17TBAQ%2BDOw4NqKpEDFYUi3wRhC%2Baj5%2FaTwaaOsbSSQ1WVXW9J%2FkDFGuUgFScfYqdG0aRaazztSFianGDgj1FEpVC%2FwLMP8LEWFghexDo2fLhZpoaNA5v8DQIvrvb839ZJGlCB9HEcbyeStsLWWrl8pM1lYBckbsmir72eSqxkPqyFfdxni2pG4HcCVuJHe6pPJwoGPGoTndv1mCghHzuk9rvPiegQ9iaKu947uL9xnhB1c7TzMUf2EGPeKuB2jm4F5duW8V3IzqQjPf3tMPSRNn8Ztv1qlO8vUhpXTsyI5dH%2BURZTqOVp0fVn4En6CRNrkv05g%2B1rxq6b6gQmlfUeAIPaTwUfI2glYGtZKNkvlkYrZKoWvHkv9XzLd3%2FKiuaeKxM9nk4hZjJqtcWwaD3Gp9yr63IUPqUZW5BI2YJHNW%2B9SIbRzBmubE0b01LVFubW9rJo3hPtgKRHPpIEIm0j%2FjoszFdpyL4chFaML0HxrCmQeh7HkvJBMvERUM%2F882V%2FBm2zHRuKsPNpLUj%2BJ%2BDh6%2FQaE0pmoIOfTbNN8xIwnaLX%2BAU67AFOxpNyX5biC5h3HLfyBGY1KrsDmnyo3bcOIqAwepos4Dw%2BlQ8II9AjVQwzEJGvd8LQ9sYaZzntH7rnuZG15wBizwUDkD2G37c91hT%2BG9hKPKHkW9jZp6XijVMHYWhd34TF6iW%2BQSM5bMzKdQaXzoilRts%2B5DaLCeYk%2Fzc2FFcSMOT3pXBXWHr%2F16lr5Sp8Gh9FS9HnwI2O8pxy6E1lqGM0wP%2FwaWBT3HgdR2tvjQzn%2BcjDDKHABtqj3oo6janO3IKOPaFzYHFqiL0DS8Pet5gUVYynm9m37o5M3%2B6y7YoaXnoq1o1goiX6Zv0S5A%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20200720T183148Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYWL2N32C3%2F20200720%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=99c8222a858071c0b65fa6dac4ea0e1b548c1a4eb23ccac06cf864c7013a7593&hash=d2c7df4c2396ff204caa66ac0553e18f1b2712399c07b6e674199859ccc1b7f9&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S002001902030082X&tid=spdf-742da070-9da3-43f1-861e-f733776129ef&sid=76772d953af62548ae38ab517857dea18c60gxrqb&type=client + + + 2020-07-20 18:31:53 + 1 + application/pdf + + conferencePaper - 2014 IEEE Conference on Computational Intelligence and Games - DOI 10.1109/cig.2014.6932884 + 2009 IEEE Symposium on Computational Intelligence and Games + DOI 10.1109/cig.2009.5286501 @@ -114,40 +420,40 @@ - Guhe - Markus + Ward + C. D. - Lascarides - Alex + Cowling + P. I. - - Game strategies for The Settlers of Catan - August 2014 + + Monte Carlo search applied to card selection in Magic: The Gathering + September 2009 - https://doi.org/10.1109%2Fcig.2014.6932884 + https://doi.org/10.1109%2Fcig.2009.5286501 - + attachment - Submitted Version + Full Text - https://www.pure.ed.ac.uk/ws/files/19351482/CIG2014_GS.pdf + https://dacemirror.sci-hub.se/proceedings-article/dfcfc3f5502682650ac71b68af8f9b19/ward2009.pdf#view=FitH - 2020-07-20 18:24:09 + 2020-07-20 18:29:50 1 application/pdf - + bookSection @@ -163,109 +469,66 @@ - Szita - István + Demaine + Erik D. - Chaslot - Guillaume + Demaine + Martin L. - Spronck - Pieter + Uehara + Ryuhei + + + + + Uno + Takeaki + + + + + Uno + Yushi - - Monte-Carlo Tree Search in Settlers of Catan + + UNO Is Hard, Even for a Single Player 2010 - https://doi.org/10.1007%2F978-3-642-12993-3_3 + https://doi.org/10.1007%2F978-3-642-13122-6_15 - DOI: 10.1007/978-3-642-12993-3_3 - 21–32 + DOI: 10.1007/978-3-642-13122-6_15 + 133–144 - + attachment - Full Text + Submitted Version - https://zero.sci-hub.se/5140/3f6b582d932254ee1b7d29e6e9683934/szita2010.pdf#view=FitH + https://dspace.mit.edu/bitstream/1721.1/62147/1/Demaine_UNO%20is.pdf - 2020-07-20 18:29:58 + 2020-07-20 18:36:18 1 application/pdf - - bookSection - - Multi-Agent Systems - - - - Springer International Publishing - - - - - - - Xenou - Konstantia - - - - - Chalkiadakis - Georgios - - - - - Afantenos - Stergos - - - - - - Deep Reinforcement Learning in Strategic Board Game Environments - 2019 - - - https://doi.org/10.1007%2F978-3-030-14174-5_16 - - - DOI: 10.1007/978-3-030-14174-5_16 - 233–248 - - - attachment - Accepted Version - - - https://oatao.univ-toulouse.fr/22647/1/xenou_22647.pdf - - - 2020-07-20 18:10:35 - 1 - application/pdf - - + journalArticle - 41 - Journal of the Operational Research Society - DOI 10.1057/jors.1990.2 + 31 + The College Mathematics Journal + DOI 10.1080/07468342.2000.11974103 1 @@ -273,236 +536,90 @@ - Maliphant - Sarah A. - - - - - Smith - David K. + Bosch + Robert A. - - Mini-Risk: Strategies for a Simplified Board Game - January 1990 + + Optimal Card-Collecting Strategies for Magic: The Gathering + January 2000 - https://doi.org/10.1057%2Fjors.1990.2 + https://doi.org/10.1080%2F07468342.2000.11974103 Publisher: Informa UK Limited - 9–16 + 15–21 - + attachment Full Text - https://zero.sci-hub.se/4681/0e142dbe029d345411eb5019cea0b10a/maliphant1990.pdf#view=FitH + https://zero.sci-hub.se/6795/ba844bedd2d417e4393d7af19bb3dd47/bosch2000.pdf#view=FitH - 2020-07-20 18:28:37 + 2020-07-20 18:37:22 1 application/pdf - + + journalArticle + + + 45 + Mathematics Magazine + DOI 10.1080/0025570x.1972.11976187 + 1 + + + + + + + Ash + Robert B. + + + + + Bishop + Richard L. + + + + + + Monopoly as a Markov Process + January 1972 + + + https://doi.org/10.1080%2F0025570x.1972.11976187 + + + Publisher: Informa UK Limited + 26–29 + + + attachment + Submitted Version + + + https://www.math.uiuc.edu/%7Ebishop/monopoly.pdf + + + 2020-07-20 18:37:15 + 1 + application/pdf + + conferencePaper - Proceedings of the 2002 ACM symposium on Applied computing - SAC \textquotesingle02 - DOI 10.1145/508791.508904 - - - - ACM Press - - - - - - Neves - Atila - - - - - Brasāo - Osvaldo - - - - - Rosa - Agostinho - - - - - - Learning the risk board game with classifier systems - 2002 - - - https://doi.org/10.1145%2F508791.508904 - - - - - attachment - Full Text - - - https://dacemirror.sci-hub.se/proceedings-article/f9ce3c906d4e89b8aa3b90f15f0dfe20/neves2002.pdf#view=FitH - - - 2020-07-20 18:26:40 - 1 - application/pdf - - - journalArticle - - - 70 - Mathematics Magazine - DOI 10.1080/0025570x.1997.11996573 - 5 - - - - - - - Tan - Bariş - - - - - - Markov Chains and the RISK Board Game - December 1997 - - - https://doi.org/10.1080%2F0025570x.1997.11996573 - - - Publisher: Informa UK Limited - 349–357 - - - attachment - Full Text - - - https://twin.sci-hub.se/6853/3bdc3204e08f60618dca66f19b9cd1fc/markov-chains-and-the-risk-board-game-1997.pdf#view=FitH - - - 2020-07-20 18:28:15 - 1 - application/pdf - - - journalArticle - - - 76 - Mathematics Magazine - DOI 10.1080/0025570x.2003.11953165 - 2 - - - - - - - Osborne - Jason A. - - - - - - Markov Chains for the RISK Board Game Revisited - April 2003 - - - https://doi.org/10.1080%2F0025570x.2003.11953165 - - - Publisher: Informa UK Limited - 129–135 - - - attachment - Full Text - - - https://twin.sci-hub.se/6908/ad9e3c21b4a5edae31079e43ad12c8ce/osborne2003.pdf#view=FitH - - - 2020-07-20 18:28:23 - 1 - application/pdf - - - journalArticle - - - 9 - IEEE Trans. Evol. Computat. - DOI 10.1109/tevc.2005.856211 - 6 - - - - - - - Vaccaro - J. M. - - - - - Guest - C. C. - - - - - - Planning an Endgame Move Set for the Game RISK: A Comparison of Search Algorithms - December 2005 - - - https://doi.org/10.1109%2Ftevc.2005.856211 - - - Publisher: Institute of Electrical and Electronics Engineers (IEEE) - 641–652 - - - attachment - Full Text - - - https://moscow.sci-hub.se/1819/0e61163e1173d174a5261879afc2c42d/vaccaro2005.pdf#view=FitH - - - 2020-07-20 18:31:44 - 1 - application/pdf - - - conferencePaper - - - 2018 IEEE Conference on Computational Intelligence and Games (CIG) - DOI 10.1109/cig.2018.8490419 + 2019 IEEE Conference on Games (CoG) + DOI 10.1109/cig.2019.8847944 @@ -512,42 +629,168 @@ - Gedda - Magnus + Canaan + Rodrigo - Lagerkvist - Mikael Z. + Togelius + Julian - Butler - Martin + Nealen + Andy + + + + + Menzel + Stefan - - Monte Carlo Methods for the Game Kingdomino - August 2018 + + Diverse Agents for Ad-Hoc Cooperation in Hanabi + August 2019 - https://doi.org/10.1109%2Fcig.2018.8490419 + https://doi.org/10.1109%2Fcig.2019.8847944 - + attachment Submitted Version - https://arxiv.org/pdf/1807.04458 + https://arxiv.org/pdf/1907.03840 - 2020-07-20 18:29:37 + 2020-07-20 18:11:10 + 1 + application/pdf + + + conferencePaper + + + 2019 IEEE Conference on Games (CoG) + DOI 10.1109/cig.2019.8848008 + + + + IEEE + + + + + + Walton-Rivers + Joseph + + + + + Williams + Piers R. + + + + + Bartle + Richard + + + + + + The 2018 Hanabi competition + August 2019 + + + https://doi.org/10.1109%2Fcig.2019.8848008 + + + + + attachment + Accepted Version + + + https://repository.essex.ac.uk/26898/2/hanabi.pdf + + + 2020-07-20 18:34:35 + 1 + application/pdf + + + conferencePaper + + + 2017 IEEE Congress on Evolutionary Computation (CEC) + DOI 10.1109/cec.2017.7969465 + + + + IEEE + + + + + + Walton-Rivers + Joseph + + + + + Williams + Piers R. + + + + + Bartle + Richard + + + + + Perez-Liebana + Diego + + + + + Lucas + Simon M. + + + + + + Evaluating and modelling Hanabi-playing agents + June 2017 + + + https://doi.org/10.1109%2Fcec.2017.7969465 + + + + + attachment + Accepted Version + + + https://repository.essex.ac.uk/20341/1/1704.07069v1.pdf + + + 2020-07-20 18:16:01 1 application/pdf @@ -601,7 +844,7 @@ - + How to Make the Perfect Fireworks Display: Two Strategies forHanabi December 2015 @@ -612,7 +855,7 @@ Publisher: Informa UK Limited 323–336 - + attachment Full Text @@ -624,12 +867,12 @@ 1 application/pdf - + conferencePaper - 2017 IEEE Congress on Evolutionary Computation (CEC) - DOI 10.1109/cec.2017.7969465 + 2018 IEEE Conference on Computational Intelligence and Games (CIG) + DOI 10.1109/cig.2018.8490419 @@ -639,20 +882,712 @@ - Walton-Rivers - Joseph + Gedda + Magnus - Williams - Piers R. + Lagerkvist + Mikael Z. - Bartle - Richard + Butler + Martin + + + + + + Monte Carlo Methods for the Game Kingdomino + August 2018 + + + https://doi.org/10.1109%2Fcig.2018.8490419 + + + + + attachment + Submitted Version + + + https://arxiv.org/pdf/1807.04458 + + + 2020-07-20 18:29:37 + 1 + application/pdf + + + journalArticle + + + 9 + IEEE Trans. Evol. Computat. + DOI 10.1109/tevc.2005.856211 + 6 + + + + + + + Vaccaro + J. M. + + + + + Guest + C. C. + + + + + + Planning an Endgame Move Set for the Game RISK: A Comparison of Search Algorithms + December 2005 + + + https://doi.org/10.1109%2Ftevc.2005.856211 + + + Publisher: Institute of Electrical and Electronics Engineers (IEEE) + 641–652 + + + attachment + Full Text + + + https://moscow.sci-hub.se/1819/0e61163e1173d174a5261879afc2c42d/vaccaro2005.pdf#view=FitH + + + 2020-07-20 18:31:44 + 1 + application/pdf + + + journalArticle + + + 70 + Mathematics Magazine + DOI 10.1080/0025570x.1997.11996573 + 5 + + + + + + + Tan + Bariş + + + + + + Markov Chains and the RISK Board Game + December 1997 + + + https://doi.org/10.1080%2F0025570x.1997.11996573 + + + Publisher: Informa UK Limited + 349–357 + + + attachment + Full Text + + + https://twin.sci-hub.se/6853/3bdc3204e08f60618dca66f19b9cd1fc/markov-chains-and-the-risk-board-game-1997.pdf#view=FitH + + + 2020-07-20 18:28:15 + 1 + application/pdf + + + conferencePaper + + + Proceedings of the 2002 ACM symposium on Applied computing - SAC \textquotesingle02 + DOI 10.1145/508791.508904 + + + + ACM Press + + + + + + Neves + Atila + + + + + Brasāo + Osvaldo + + + + + Rosa + Agostinho + + + + + + Learning the risk board game with classifier systems + 2002 + + + https://doi.org/10.1145%2F508791.508904 + + + + + attachment + Full Text + + + https://dacemirror.sci-hub.se/proceedings-article/f9ce3c906d4e89b8aa3b90f15f0dfe20/neves2002.pdf#view=FitH + + + 2020-07-20 18:26:40 + 1 + application/pdf + + + journalArticle + + + 41 + Journal of the Operational Research Society + DOI 10.1057/jors.1990.2 + 1 + + + + + + + Maliphant + Sarah A. + + + + + Smith + David K. + + + + + + Mini-Risk: Strategies for a Simplified Board Game + January 1990 + + + https://doi.org/10.1057%2Fjors.1990.2 + + + Publisher: Informa UK Limited + 9–16 + + + attachment + Full Text + + + https://zero.sci-hub.se/4681/0e142dbe029d345411eb5019cea0b10a/maliphant1990.pdf#view=FitH + + + 2020-07-20 18:28:37 + 1 + application/pdf + + + conferencePaper + + + 2014 IEEE Conference on Computational Intelligence and Games + DOI 10.1109/cig.2014.6932884 + + + + IEEE + + + + + + Guhe + Markus + + + + + Lascarides + Alex + + + + + + Game strategies for The Settlers of Catan + August 2014 + + + https://doi.org/10.1109%2Fcig.2014.6932884 + + + + + attachment + Submitted Version + + + https://www.pure.ed.ac.uk/ws/files/19351482/CIG2014_GS.pdf + + + 2020-07-20 18:24:09 + 1 + application/pdf + + + journalArticle + + + 76 + Mathematics Magazine + DOI 10.1080/0025570x.2003.11953165 + 2 + + + + + + + Osborne + Jason A. + + + + + + Markov Chains for the RISK Board Game Revisited + April 2003 + + + https://doi.org/10.1080%2F0025570x.2003.11953165 + + + Publisher: Informa UK Limited + 129–135 + + + attachment + Full Text + + + https://twin.sci-hub.se/6908/ad9e3c21b4a5edae31079e43ad12c8ce/osborne2003.pdf#view=FitH + + + 2020-07-20 18:28:23 + 1 + application/pdf + + + bookSection + + Multi-Agent Systems + + + + Springer International Publishing + + + + + + + Xenou + Konstantia + + + + + Chalkiadakis + Georgios + + + + + Afantenos + Stergos + + + + + + Deep Reinforcement Learning in Strategic Board Game Environments + 2019 + + + https://doi.org/10.1007%2F978-3-030-14174-5_16 + + + DOI: 10.1007/978-3-030-14174-5_16 + 233–248 + + + attachment + Accepted Version + + + https://oatao.univ-toulouse.fr/22647/1/xenou_22647.pdf + + + 2020-07-20 18:10:35 + 1 + application/pdf + + + bookSection + + + Lecture Notes in Computer Science + + + + + Springer Berlin Heidelberg + + + + + + + Szita + István + + + + + Chaslot + Guillaume + + + + + Spronck + Pieter + + + + + + Monte-Carlo Tree Search in Settlers of Catan + 2010 + + + https://doi.org/10.1007%2F978-3-642-12993-3_3 + + + DOI: 10.1007/978-3-642-12993-3_3 + 21–32 + + + attachment + Full Text + + + https://zero.sci-hub.se/5140/3f6b582d932254ee1b7d29e6e9683934/szita2010.pdf#view=FitH + + + 2020-07-20 18:29:58 + 1 + application/pdf + + + journalArticle + + + 10 + International Journal of Gaming and Computer-Mediated Simulations + DOI 10.4018/ijgcms.2018040103 + 2 + + + + + + + Boda + Márton Attila + + + + + + Avoiding Revenge Using Optimal Opponent Ranking Strategy in the Board Game Catan + April 2018 + + + https://doi.org/10.4018%2Fijgcms.2018040103 + + + Publisher: IGI Global + 47–70 + + + attachment + Full Text + + + https://sci-hub.se/downloads/2020-05-28/3d/boda2018.pdf#view=FitH + + + 2020-07-20 18:22:11 + 1 + application/pdf + + + conferencePaper + + + 2014 IEEE Conference on Computational Intelligence and Games + DOI 10.1109/cig.2014.6932861 + + + + IEEE + + + + + + Guhe + Markus + + + + + Lascarides + Alex + + + + + + The effectiveness of persuasion in The Settlers of Catan + August 2014 + + + https://doi.org/10.1109%2Fcig.2014.6932861 + + + + + attachment + Submitted Version + + + https://www.pure.ed.ac.uk/ws/files/19353900/CIG2014.pdf + + + 2020-07-20 18:34:57 + 1 + application/pdf + + + journalArticle + + + + + + Osawa + Hirotaka + + + + + Kawagoe + Atsushi + + + + + Sato + Eisuke + + + + + Kato + Takuya + + + + + + Emergence of Cooperative Impression With Self-Estimation, Thinking Time, and Concordance of Risk Sensitivity in Playing Hanabi + The authors evaluate the extent to which a user’s impression of an AI agent can be improved by giving the agent the ability of self-estimation, thinking time, and coordination of risk tendency. The authors modified the algorithm of an AI agent in the cooperative game Hanabi to have all of these traits, and investigated the change in the user’s impression by playing with the user. The authors used a self-estimation task to evaluate the effect that the ability to read the intention of a user had on an impression. The authors also show thinking time of an agent influences impression for an agent. The authors also investigated the relationship between the concordance of the risk-taking tendencies of players and agents, the player’s impression of agents, and the game experience. The results of the self-estimation task experiment showed that the more accurate the estimation of the agent’s self, the more likely it is that the partner will perceive humanity, affinity, intelligence, and communication skills in the agent. The authors also found that an agent that changes the length of thinking time according to the priority of action gives the impression that it is smarter than an agent with a normal thinking time when the player notices the difference in thinking time or an agent that randomly changes the thinking time. The result of the experiment regarding concordance of the risk-taking tendency shows that influence player’s impression toward agents. These results suggest that game agent designers can improve the player’s disposition toward an agent and the game experience by adjusting the agent’s self-estimation level, thinking time, and risk-taking tendency according to the player’s personality and inner state during the game. + 2021-10-12 + DOI.org (Crossref) + + + https://www.frontiersin.org/articles/10.3389/frobt.2021.658348/full + + + 2021-11-24 07:14:38 + 658348 + + + 8 + Frontiers in Robotics and AI + DOI 10.3389/frobt.2021.658348 + Front. Robot. AI + ISSN 2296-9144 + + + attachment + Full Text + + + https://www.frontiersin.org/articles/10.3389/frobt.2021.658348/pdf + + + 2021-11-24 07:15:06 + 1 + application/pdf + + + thesis + + + Örebro University, School of Science and Technology. + + + + + + + Nguyen, Van Hoa + + + + + A Graphical User Interface For The Hanabi Challenge Benchmark + This report will describe the development of the Graphical User Interface (GUI) forthe Hanabi Challenge Benchmark. The benchmark is based on the popular cardgame Hanabi and presents itself as a new research frontier in artificial intelligencefor cooperative multi-agent challenges. The project’s intentions and goals are tointerpret and visualize the data output from the benchmark to give us a better understandingof it.A GUI was then developed by using knowledge within theory of mind in combinationwith theories within human-computer interaction. The results of this project wereevaluated through a small-scale usability test. Users of different ages, gender andlevels of computer knowledge tested the application and through a questionnaire,the quality of the GUI was assessed. + + + http://oru.diva-portal.org/smash/record.jsf?pid=diva2%3A1597503 + + + + + computerProgram + + + + + Raluca D. Gaina + + + + + Martin Balla + + + + + Alexander Dockhorn + + + + + Raul Montoliu + + + + + Diego Perez-Liebana + + + + + TAG: Tabletop Games Framework + The Tabletop Games Framework (TAG) is a Java-based benchmark for developing modern board games for AI research. TAG provides a common skeleton for implementing tabletop games based on a common API for AI agents, a set of components and classes to easily add new games and an import module for defining data in JSON format. At present, this platform includes the implementation of seven different tabletop games that can also be used as an example for further developments. Additionally, TAG also incorporates logging functionality that allows the user to perform a detailed analysis of the game, in terms of action space, branching factor, hidden information, and other measures of interest for Game AI research. + + + https://github.com/GAIGResearch/TabletopGames + + + MIT License + Java + + + computerProgram + + + + + Adam Stelmaszczyk + + + + + Game Tree Search Algorithms - C++ library for AI bot programming. + 2015 + Game Tree Search Algorithms + + + https://github.com/AdamStelmaszczyk/gtsa + + + C++ + + + journalArticle + + arXiv:2009.12065 [cs] + + + + + + Gaina + Raluca D. + + + + + Balla + Martin + + + + + Dockhorn + Alexander + + + + + Montoliu + Raul @@ -661,32 +1596,4902 @@ Diego + + + + + + + Computer Science - Artificial Intelligence + + + Design and Implementation of TAG: A Tabletop Games Framework + This document describes the design and implementation of the Tabletop Games framework (TAG), a Java-based benchmark for developing modern board games for AI research. TAG provides a common skeleton for implementing tabletop games based on a common API for AI agents, a set of components and classes to easily add new games and an import module for defining data in JSON format. At present, this platform includes the implementation of seven different tabletop games that can also be used as an example for further developments. Additionally, TAG also incorporates logging functionality that allows the user to perform a detailed analysis of the game, in terms of action space, branching factor, hidden information, and other measures of interest for Game AI research. The objective of this document is to serve as a central point where the framework can be described at length. TAG can be downloaded at: https://github.com/GAIGResearch/TabletopGames + 2020-09-25 + Design and Implementation of TAG + arXiv.org + + + http://arxiv.org/abs/2009.12065 + + + 2021-07-24 08:41:01 + arXiv: 2009.12065 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/2009.12065 + + + 2021-07-24 08:41:11 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/2009.12065.pdf + + + 2021-07-24 08:41:07 + 1 + application/pdf + + + journalArticle + + arXiv:1910.04376 [cs] + + + - Lucas - Simon M. + Zha + Daochen + + + + + Lai + Kwei-Herng + + + + + Cao + Yuanpu + + + + + Huang + Songyi + + + + + Wei + Ruzhe + + + + + Guo + Junyu + + + + + Hu + Xia - - Evaluating and modelling Hanabi-playing agents - June 2017 + + + + + Computer Science - Artificial Intelligence + + + RLCard: A Toolkit for Reinforcement Learning in Card Games + RLCard is an open-source toolkit for reinforcement learning research in card games. It supports various card environments with easy-to-use interfaces, including Blackjack, Leduc Hold'em, Texas Hold'em, UNO, Dou Dizhu and Mahjong. The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with multiple agents, large state and action space, and sparse reward. In this paper, we provide an overview of the key components in RLCard, a discussion of the design principles, a brief introduction of the interfaces, and comprehensive evaluations of the environments. The codes and documents are available at https://github.com/datamllab/rlcard + 2020-02-14 + RLCard + arXiv.org - https://doi.org/10.1109%2Fcec.2017.7969465 + http://arxiv.org/abs/1910.04376 + + + 2021-07-24 08:40:55 + arXiv: 1910.04376 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/1910.04376 + + + 2021-07-24 08:41:03 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/1910.04376.pdf + + + 2021-07-24 08:40:59 + 1 + application/pdf + + + presentation + + + + + Christoffer Limér + + + + + Erik Kalmér + + + + + + Wargaming with Monte-Carlo Tree Search + 2/16/2021 + en + + + https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-SAS-OCS-ORA-2020/MP-SAS-OCS-ORA-2020-WCM-01P.pdf + + + + + attachment + Full Text + + + https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-SAS-OCS-ORA-2020/MP-SAS-OCS-ORA-2020-WCM-01P.pdf + + + 2021-07-24 08:35:04 + 1 + application/pdf + + + conferencePaper + + + ISBN 978-92-837-2336-3 + 14th NATO Operations Research and Analysis (OR&A) Conference: Emerging and Disruptive Technology + DOI 10.14339/STO-MP-SAS-OCS-ORA-2020-WCM-01-PDF + + + + NATO + + + + + + Christoffer Limér + + + + + Erik Kalmér + + + + + Mika Cohen + + + + + + Monte Carlo Tree Search for Risk + 2/16/2021 + en + AC/323(SAS-ACT)TP/1017 + + + https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-SAS-OCS-ORA-2020/MP-SAS-OCS-ORA-2020-WCM-01.pdf - + attachment - Accepted Version + Full Text - https://repository.essex.ac.uk/20341/1/1704.07069v1.pdf + https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-SAS-OCS-ORA-2020/MP-SAS-OCS-ORA-2020-WCM-01.pdf - 2020-07-20 18:16:01 + 2021-07-24 08:34:15 + 1 + application/pdf + + + thesis + + + Blekinge Institute of Technology, School of Engineering, Department of Systems and Software Engineering. + + + + + + + Olsson + Fredrik + + + + + + + A multi-agent system for playing the board game risk + Risk is a game in which traditional Artificial-Intelligence methods such as for example iterative deepening and Alpha-Beta pruning can not successfully be applied due to the size of the search space. Distributed problem solving in the form of a multi-agent system might be the solution. This needs to be tested before it is possible to tell if a multi-agent system will be successful at playing Risk or not. In this thesis the development of a multi-agent system that plays Risk is explained. The system places an agent in every country on the board and uses a central agent for organizing communication. An auction mechanism is used for negotiation. The experiments show that a multi-agent solution indeed is a prosperous approach when developing a computer based player for the board game Risk. + 2005 + + + http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3781 + + + 51 + Independent thesis Advanced level (degree of Master (One Year)) + + + attachment + Full Text + + + https://www.diva-portal.org/smash/get/diva2:831093/FULLTEXT01.pdf + + + 2021-07-24 08:28:25 + 3 + + + attachment + Full Text + + + http://bth.diva-portal.org/smash/get/diva2:831093/FULLTEXT01 + + + 2021-07-24 08:26:48 + 1 + application/pdf + + + presentation + + + + + Michael Wolf + + + + + + An Intelligent Artificial Player for the Game of Risk + 20/04/2005 + + + http://www.ke.tu-darmstadt.de/lehre/archiv/ss04/oberseminar/folien/Wolf_Michael-Slides.pdf + + + + + attachment + An Intelligent Artificial Player for the Game of R.pdf + application/pdf + + + journalArticle + + arXiv:1511.08099 [cs] + + + + + + Cuayáhuitl + Heriberto + + + + + Keizer + Simon + + + + + Lemon + Oliver + + + + + + + + + Computer Science - Artificial Intelligence + + + + + Computer Science - Machine Learning + + + Strategic Dialogue Management via Deep Reinforcement Learning + Artificially intelligent agents equipped with strategic skills that can negotiate during their interactions with other natural or artificial agents are still underdeveloped. This paper describes a successful application of Deep Reinforcement Learning (DRL) for training intelligent agents with strategic conversational skills, in a situated dialogue setting. Previous studies have modelled the behaviour of strategic agents using supervised learning and traditional reinforcement learning techniques, the latter using tabular representations or learning with linear function approximation. In this study, we apply DRL with a high-dimensional state space to the strategic board game of Settlers of Catan---where players can offer resources in exchange for others and they can also reply to offers made by other players. Our experimental results report that the DRL-based learnt policies significantly outperformed several baselines including random, rule-based, and supervised-based behaviours. The DRL-based policy has a 53% win rate versus 3 automated players (`bots'), whereas a supervised player trained on a dialogue corpus in this setting achieved only 27%, versus the same 3 bots. This result supports the claim that DRL is a promising framework for training dialogue systems, and strategic agents with negotiation abilities. + 2015-11-25 + arXiv.org + + + http://arxiv.org/abs/1511.08099 + + + 2021-07-24 08:23:51 + arXiv: 1511.08099 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/1511.08099 + + + 2021-07-24 08:24:01 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/1511.08099.pdf + + + 2021-07-24 08:23:57 + 1 + application/pdf + + + report + + + + + Nguyen + Cuong + + + + + Dinjian + Daniel + + + + + + The Difficulty of Learning Ticket to Ride + Ticket to Ride is a very popular, award-winning board-game where you try toscore the most points while building a railway spanning cities in America. For acomputer to learn to play this game is very difficult due to the vast state-actionspace. This project will explain why featurizing your state, and implementingcurriculum learning can help agents learn as state-action spaces grow too largefor traditional learning methods to be effective. + + + https://www.eecs.tufts.edu/~jsinapov/teaching/comp150_RL/reports/Nguyen_Dinjian_report.pdf + + + + + attachment + Full Text + + + https://www.eecs.tufts.edu/~jsinapov/teaching/comp150_RL/reports/Nguyen_Dinjian_report.pdf + + + 2021-07-24 08:19:13 + 1 + application/pdf + + + presentation + + + + + Middlebury College + + + + + + + + + R. Teal Witter + + + + + Alex Lyford + + + + + + Applications of Graph Theory and Probability in the Board Game Ticket to Ride + January 16, 2020 + + + https://www.rtealwitter.com/slides/2020-JMM.pdf + + + + + attachment + Full Text + + + https://www.rtealwitter.com/slides/2020-JMM.pdf + + + 2021-07-24 08:18:37 + 1 + application/pdf + + + report + + + + + Glenn + James + + + + + An Optimal Strategy for Yahtzee + + + http://www.cs.loyola.edu/~jglenn/research/optimal_yahtzee.pdf + + + + + report + + + KTH Royal Institute Of Technology Computer Science And Communication + + + Defensive Yahtzee + In this project an algorithm has been created that plays Yahtzee using rule +based heuristics. The focus is getting a high lowest score and a high 10th +percentile. All rules of Yahtzee and the probabilities for each combination +have been studied and based on this each turn is optimized to get a +guaranteed decent high score. The algorithm got a lowest score of 79 and a +10th percentile of 152 when executed 100 000 times. + https://www.diva-portal.org/smash/get/diva2:817838/FULLTEXT01.pdf + + + http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168668 + + + 22 + + + thesis + + + Yale University, Department of Computer Science + + + + + + + Vasseur + Philip + + + + + Using Deep Q-Learning to Compare Strategy Ladders of Yahtzee + “Bots” playing games is not a new concept, +likely going back to the first video games. However, +there has been a new wave recently using machine +learning to learn to play games at a near optimal +level - essentially using neural networks to “solve” +games. Depending on the game, this can be relatively +straight forward using supervised learning. However, +this requires having data for optimal play, which is +often not possible due to the sheer complexity of many +games. For example, solitaire Yahtzee has this data +available, but two player Yahtzee does not due to the +massive state space. A recent trend in response to this +started with Google Deep Mind in 2013, who used Deep +Reinforcement Learning to play various Atari games +[4]. +This project will apply Deep Reinforcement Learning +(specifically Deep Q-Learning) and measure how an +agent learns to play Yahtzee in the form of a strategy +ladder. A strategy ladder is a way of looking at how +the performance of an AI varies with the computational +resources it uses. Different sets of rules changes how the +the AI learns which varies the strategy ladder itself. This +project will vary the upper bonus threshold and then +attempt to measure how “good” the various strategy +ladders are - in essence attempting to find the set of +rules which creates the “best” version of Yahtzee. We +assume/expect that there is some correlation between +strategy ladders for AI and strategy ladders for human, +meaning that a game with a “good” strategy ladder for +an AI indicates that game is interesting and challenging +for humans. + December 12, 2019 + en + + + https://raw.githubusercontent.com/philvasseur/Yahtzee-DQN-Thesis/dcf2bfe15c3b8c0ff3256f02dd3c0aabdbcbc9bb/webpage/final_report.pdf + + + 12 + + + manuscript + + + + + Verhoeff + Tom + + + + + How to Maximize Your Score in Solitaire Yahtzee + Yahtzee is a well-known game played with five dice. Players take turns at assembling and scoring dice patterns. The player with the highest score wins. Solitaire Yahtzee is a single-player version of Yahtzee aimed at maximizing one’s score. A strategy for playing Yahtzee determines which choice to make in each situation of the game. We show that the maximum expected score over all Solitaire Yahtzee strategies is 254.5896. . . . + en + + + http://www-set.win.tue.nl/~wstomv/misc/yahtzee/yahtzee-report-unfinished.pdf + + + 18 + Incomplete Draft + + + thesis + + + KTH, School of Computer Science and Communication (CSC) + + + + + + + Serra + Andreas + + + + + Niigata + Kai Widell + + + + + Optimal Yahtzee performance in multi-player games + Yahtzee is a game with a moderately large search space, dependent on the factor of luck. This makes it not quite trivial to implement an optimal strategy for it. Using the optimal strategy for single-player +use, comparisons against other algorithms are made and the results are analyzed for hints on what it could take to make an algorithm that could beat the single-player optimal strategy. + April 12, 2013 + en + http://www.diva-portal.org/smash/get/diva2:668705/FULLTEXT01.pdf + + + https://www.csc.kth.se/utbildning/kth/kurser/DD143X/dkand13/Group4Per/report/12-serra-widell-nigata.pdf + + + 17 + Independent thesis Basic level (degree of Bachelor) + + + journalArticle + + + + + + + + Bonarini + Andrea + + + + + Lazaric + Alessandro + + + + + Restelli + Marcello + + + + + Yahtzee: a Large Stochastic Environment for RL Benchmarks + Yahtzee is a game that is regularly played by more than 100 million people in the world. We +propose a simplified version of Yahtzee as a benchmark for RL algorithms. We have already +used it for this purpose, and an implementation is available. + + + http://researchers.lille.inria.fr/~lazaric/Webpage/PublicationsByTopic_files/bonarini2005yahtzee.pdf + + + 1 + + + presentation + + + + + Verhoeff + Tom + + + + + Optimal Solitaire Yahtzee Strategies + + + http://www.yahtzee.org.uk/optimal_yahtzee_TV.pdf + + + + + journalArticle + + + + + + Litwiller + Bonnie H. + + + + + Duncan + David R. + + + + + Probabilites In Yahtzee + Teachers of units in probability are often interested in providing examples of probabilistic situations in a nonclassroom setting. Games are a rich source of such probabilities. Many people enjoy playing a commercial game called Yahtzee. A Yahtzee player receives points for achieving various specified numerical combinations of five dice during the three rolls that constitute a turn. + 12/1982 + DOI.org (Crossref) + + + https://pubs.nctm.org/view/journals/mt/75/9/article-p751.xml + + + 2021-07-24 07:53:57 + 751-754 + + + 75 + The Mathematics Teacher + DOI 10.5951/MT.75.9.0751 + 9 + MT + ISSN 0025-5769, 2330-0582 + + + journalArticle + + arXiv:2107.07630 [cs] + + + + + + Siu + Ho Chit + + + + + Pena + Jaime D. + + + + + Chang + Kimberlee C. + + + + + Chen + Edenna + + + + + Zhou + Yutai + + + + + Lopez + Victor J. + + + + + Palko + Kyle + + + + + Allen + Ross E. + + + + + + + + + Computer Science - Artificial Intelligence + + + + + Computer Science - Human-Computer Interaction + + + Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi + Deep reinforcement learning has generated superhuman AI in competitive games such as Go and StarCraft. Can similar learning techniques create a superior AI teammate for human-machine collaborative games? Will humans prefer AI teammates that improve objective team performance or those that improve subjective metrics of trust? In this study, we perform a single-blind evaluation of teams of humans and AI agents in the cooperative card game Hanabi, with both rule-based and learning-based agents. In addition to the game score, used as an objective metric of the human-AI team performance, we also quantify subjective measures of the human's perceived performance, teamwork, interpretability, trust, and overall preference of AI teammate. We find that humans have a clear preference toward a rule-based AI teammate (SmartBot) over a state-of-the-art learning-based AI teammate (Other-Play) across nearly all subjective metrics, and generally view the learning-based agent negatively, despite no statistical difference in the game score. This result has implications for future AI design and reinforcement learning benchmarking, highlighting the need to incorporate subjective metrics of human-AI teaming rather than a singular focus on objective task performance. + 2021-07-19 + arXiv.org + + + http://arxiv.org/abs/2107.07630 + + + 2021-07-24 06:30:44 + arXiv: 2107.07630 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/2107.07630 + + + 2021-07-24 06:31:06 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/2107.07630.pdf + + + 2021-07-24 06:31:01 + 1 + application/pdf + + + conferencePaper + + + 6 + ISBN 2342-9666 + Think Design Play + + + + + DiGRA/Utrecht School of the Arts + + + Exploring anonymity in cooperative board games + This study was done as a part of a larger research project where the interest was on exploring if and how gameplay design could give informative principles to the design of educational activities. The researchers conducted a series of studies trying to map game mechanics that had the special quality of being inclusive, i.e., playable by a diverse group of players. This specific study focused on designing a cooperative board game with the goal of implementing anonymity as a game mechanic. Inspired by the gameplay design patterns methodology (Björk & Holopainen 2005a; 2005b; Holopainen & Björk 2008), mechanics from existing cooperative board games were extracted and analyzed in order to inform the design process. The results from prototyping and play testing indicated that it is possible to implement anonymous actions in cooperative board games and that this mechanic made rather unique forms of gameplay possible. These design patterns can be further developed in order to address inclusive educational practices. + January 2011 + + + http://www.digra.org/digital-library/publications/exploring-anonymity-in-cooperative-board-games/ + + + + + 2011 DiGRA International Conference + + + + + conferencePaper + + + + MDA: A Formal Approach to Game Design and Game Research + + + https://aaai.org/Library/Workshops/2004/ws04-04-001.php + + + + + journalArticle + + + DOI 10.4230/LIPICS.FUN.2016.1 + + + + + + + Abdelkader + Ahmed + + + + + Acharya + Aditya + + + + + Dasler + Philip + + + + + + + + + Herbstritt + Marc + + + + + + + 000 Computer science, knowledge, general works + + + + + Computer Science + + + 2048 Without New Tiles Is Still Hard + 2016 + en + DOI.org (Datacite) + + + http://drops.dagstuhl.de/opus/volltexte/2016/5885/ + + + 2021-06-28 16:09:58 + Artwork Size: 14 pages +Medium: application/pdf +Publisher: Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH, Wadern/Saarbruecken, Germany + 14 pages + + + journalArticle + + arXiv:1501.03837 [cs] + + + + + + Abdelkader + Ahmed + + + + + Acharya + Aditya + + + + + Dasler + Philip + + + + + + + + + Computer Science - Computational Complexity + + + + F.2.2 + + On the Complexity of Slide-and-Merge Games + We study the complexity of a particular class of board games, which we call `slide and merge' games. Namely, we consider 2048 and Threes, which are among the most popular games of their type. In both games, the player is required to slide all rows or columns of the board in one direction to create a high value tile by merging pairs of equal tiles into one with the sum of their values. This combines features from both block pushing and tile matching puzzles, like Push and Bejeweled, respectively. We define a number of natural decision problems on a suitable generalization of these games and prove NP-hardness for 2048 by reducing from 3SAT. Finally, we discuss the adaptation of our reduction to Threes and conjecture a similar result. + 2015-01-15 + arXiv.org + + + http://arxiv.org/abs/1501.03837 + + + 2021-06-28 16:09:34 + arXiv: 1501.03837 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/1501.03837 + + + 2021-06-28 16:09:52 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/1501.03837.pdf + + + 2021-06-28 16:09:48 + 1 + application/pdf + + + conferencePaper + + + ISBN 978-1-4799-3547-5 + 2014 IEEE Conference on Computational Intelligence and Games + DOI 10.1109/CIG.2014.6932907 + + + + + + + Dortmund, Germany + + + IEEE + + + + + + + Szubert + Marcin + + + + + Jaskowski + Wojciech + + + + + + Temporal difference learning of N-tuple networks for the game 2048 + 8/2014 + DOI.org (Crossref) + + + http://ieeexplore.ieee.org/document/6932907/ + + + 2021-06-28 16:09:20 + 1-8 + + + 2014 IEEE Conference on Computational Intelligence and Games (CIG) + + + + + attachment + Submitted Version + + + https://www.cs.put.poznan.pl/mszubert/pub/szubert2014cig.pdf + + + 2021-06-28 16:09:26 + 1 + application/pdf + + + report + The complexity of Scotland Yard + + + https://eprints.illc.uva.nl/id/eprint/193/1/PP-2006-18.text.pdf + + + + + computerProgram + A Mathematical Analysis of the Game of Santorini + + + https://github.com/carsongeissler/SantoriniIS + + + + + thesis + A Mathematical Analysis of the Game of Santorini + + + https://openworks.wooster.edu/independentstudy/8917/ + + + + + journalArticle + + + arXiv:1906.02330 [cs, stat] + + + + + + + Serrino + Jack + + + + + Kleiman-Weiner + Max + + + + + Parkes + David C. + + + + + Tenenbaum + Joshua B. + + + + + + + + + Computer Science - Machine Learning + + + + + Computer Science - Multiagent Systems + + + + + Statistics - Machine Learning + + + Finding Friend and Foe in Multi-Agent Games + Recent breakthroughs in AI for multi-agent games like Go, Poker, and Dota, have seen great strides in recent years. Yet none of these games address the real-life challenge of cooperation in the presence of unknown and uncertain teammates. This challenge is a key game mechanism in hidden role games. Here we develop the DeepRole algorithm, a multi-agent reinforcement learning agent that we test on The Resistance: Avalon, the most popular hidden role game. DeepRole combines counterfactual regret minimization (CFR) with deep value networks trained through self-play. Our algorithm integrates deductive reasoning into vector-form CFR to reason about joint beliefs and deduce partially observable actions. We augment deep value networks with constraints that yield interpretable representations of win probabilities. These innovations enable DeepRole to scale to the full Avalon game. Empirical game-theoretic methods show that DeepRole outperforms other hand-crafted and learned agents in five-player Avalon. DeepRole played with and against human players on the web in hybrid human-agent teams. We find that DeepRole outperforms human players as both a cooperator and a competitor. + 2019-06-05 + arXiv.org + + + http://arxiv.org/abs/1906.02330 + + + 2021-06-28 16:00:28 + arXiv: 1906.02330 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/1906.02330 + + + 2021-06-28 16:00:38 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/1906.02330.pdf + + + 2021-06-28 16:00:35 + 1 + application/pdf + + + thesis + On Solving Pentago + + + http://www.ke.tu-darmstadt.de/lehre/arbeiten/bachelor/2011/Buescher_Niklas.pdf + + + + + journalArticle + + + + + + Nakai + Kenichiro + + + + + Takenaga + Yasuhiko + + + + + + NP-Completeness of Pandemic + 2012 + en + DOI.org (Crossref) + + + https://www.jstage.jst.go.jp/article/ipsjjip/20/3/20_723/_article + + + 2021-06-28 15:59:47 + 723-726 + + + 20 + Journal of Information Processing + DOI 10.2197/ipsjjip.20.723 + 3 + Journal of Information Processing + ISSN 1882-6652 + + + attachment + Full Text + + + https://www.jstage.jst.go.jp/article/ipsjjip/20/3/20_723/_pdf + + + 2021-06-28 15:59:50 + 1 + application/pdf + + + presentation + What’s the Best Monopoly Strategy? + + + https://core.ac.uk/download/pdf/48614184.pdf + + + + + conferencePaper + + + + Learning to play Monopoly: A Reinforcement Learning approach + + + https://intelligence.csd.auth.gr/publication/conference-papers/learning-to-play-monopoly-a-reinforcement-learning-approach/ + + + + + report + A Markovian Exploration of Monopoly + + + https://pi4.math.illinois.edu/wp-content/uploads/2014/10/Gartland-Burson-Ferguson-Markovopoly.pdf + + + + + conferencePaper + + + ISBN 978-1-72811-895-6 + TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) + DOI 10.1109/TENCON.2019.8929523 + + + + + + + Kochi, India + + + IEEE + + + + + + + Arun + Edupuganti + + + + + Rajesh + Harikrishna + + + + + Chakrabarti + Debarka + + + + + Cherala + Harikiran + + + + + George + Koshy + + + + + + Monopoly Using Reinforcement Learning + 10/2019 + DOI.org (Crossref) + + + https://ieeexplore.ieee.org/document/8929523/ + + + 2021-06-28 15:49:50 + 858-862 + + + TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) + + + + + attachment + Full Text + + + https://sci-hub.se/downloads/2020-04-10/35/arun2019.pdf?rand=60d9ef9f20b26#view=FitH + + + 2021-06-28 15:50:07 + 1 + application/pdf + + + report + Learning to Play Monopoly with Monte Carlo Tree Search + + + https://project-archive.inf.ed.ac.uk/ug4/20181042/ug4_proj.pdf + + + + + conferencePaper + + + ISBN 978-1-4244-5770-0 978-1-4244-5771-7 + Proceedings of the 2009 Winter Simulation Conference (WSC) + DOI 10.1109/WSC.2009.5429349 + + + + + + + Austin, TX, USA + + + IEEE + + + + + + + Friedman + Eric J. + + + + + Henderson + Shane G. + + + + + Byuen + Thomas + + + + + Gallardo + German Gutierrez + + + + + + Estimating the probability that the game of Monopoly never ends + 12/2009 + DOI.org (Crossref) + + + http://ieeexplore.ieee.org/document/5429349/ + + + 2021-06-28 15:49:23 + 380-391 + + + 2009 Winter Simulation Conference (WSC 2009) + + + + + attachment + Full Text + + + https://moscow.sci-hub.se/3233/bacac19e84c764b72c627d05f55c0ad9/friedman2009.pdf#view=FitH + + + 2021-06-28 15:49:32 + 1 + application/pdf + + + conferencePaper + + + ISBN 978-1-4673-1194-6 978-1-4673-1193-9 978-1-4673-1192-2 + 2012 IEEE Conference on Computational Intelligence and Games (CIG) + DOI 10.1109/CIG.2012.6374168 + + + + + + + Granada, Spain + + + IEEE + + + + + + + Friberger + Marie Gustafsson + + + + + Togelius + Julian + + + + + + Generating interesting Monopoly boards from open data + 09/2012 + DOI.org (Crossref) + + + http://ieeexplore.ieee.org/document/6374168/ + + + 2021-06-28 15:49:18 + 288-295 + + + 2012 IEEE Conference on Computational Intelligence and Games (CIG) + + + + + attachment + Submitted Version + + + http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=81CA58D9ACCE8CA7412077093E520EFC?doi=10.1.1.348.6099&rep=rep1&type=pdf + + + 2021-06-28 15:49:32 + 1 + application/pdf + + + conferencePaper + + + ISBN 978-0-7803-7203-0 + Proceedings 2001 IEEE International Symposium on Computational Intelligence in Robotics and Automation (Cat. No.01EX515) + DOI 10.1109/CIRA.2001.1013210 + + + + + + + Banff, Alta., Canada + + + IEEE + + + + + + + Yasumura + Y. + + + + + Oguchi + K. + + + + + Nitta + K. + + + + + + Negotiation strategy of agents in the MONOPOLY game + 2001 + DOI.org (Crossref) + + + http://ieeexplore.ieee.org/document/1013210/ + + + 2021-06-28 15:49:10 + 277-281 + + + 2001 International Symposium on Computational Intelligence in Robotics and Automation + + + + + attachment + Full Text + + + https://moscow.sci-hub.se/3317/19346a5b777c1582800b51ee3a7cf5ed/negotiation-strategy-of-agents-in-the-monopoly-game.pdf#view=FitH + + + 2021-06-28 15:49:15 + 1 + application/pdf + + + journalArticle + + arXiv:2103.00683 [cs] + + + + + + Haliem + Marina + + + + + Bonjour + Trevor + + + + + Alsalem + Aala + + + + + Thomas + Shilpa + + + + + Li + Hongyu + + + + + Aggarwal + Vaneet + + + + + Bhargava + Bharat + + + + + Kejriwal + Mayank + + + + + + + + + Computer Science - Artificial Intelligence + + + + + Computer Science - Machine Learning + + + Learning Monopoly Gameplay: A Hybrid Model-Free Deep Reinforcement Learning and Imitation Learning Approach + Learning how to adapt and make real-time informed decisions in dynamic and complex environments is a challenging problem. To learn this task, Reinforcement Learning (RL) relies on an agent interacting with an environment and learning through trial and error to maximize the cumulative sum of rewards received by it. In multi-player Monopoly game, players have to make several decisions every turn which involves complex actions, such as making trades. This makes the decision-making harder and thus, introduces a highly complicated task for an RL agent to play and learn its winning strategies. In this paper, we introduce a Hybrid Model-Free Deep RL (DRL) approach that is capable of playing and learning winning strategies of the popular board game, Monopoly. To achieve this, our DRL agent (1) starts its learning process by imitating a rule-based agent (that resembles the human logic) to initialize its policy, (2) learns the successful actions, and improves its policy using DRL. Experimental results demonstrate an intelligent behavior of our proposed agent as it shows high win rates against different types of agent-players. + 2021-02-28 + Learning Monopoly Gameplay + arXiv.org + + + http://arxiv.org/abs/2103.00683 + + + 2021-06-28 15:48:08 + arXiv: 2103.00683 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/2103.00683 + + + 2021-06-28 15:48:23 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/2103.00683.pdf + + + 2021-06-28 15:48:19 + 1 + application/pdf + + + computerProgram + A constraint programming based solver for Modern Art + + + https://github.com/captn3m0/modernart + + + + + report + Magic: The Gathering Deck Performance Prediction + + + http://cs229.stanford.edu/proj2012/HauPlotkinTran-MagicTheGatheringDeckPerformancePrediction.pdf + + + + + thesis + Deckbuilding in Magic: The Gathering Using a Genetic Algorithm + + + https://doi.org/11250/2462429 + + + + + conferencePaper + + + + Deck Construction Strategies for Magic: The Gathering + + + https://www.doi.org/10.1685/CSC06077 + + + + + thesis + Mathematical programming and Magic: The Gathering + + + https://commons.lib.niu.edu/handle/10843/19194 + + + + + computerProgram + Magic: The Gathering in Common Lisp + + + https://github.com/jeffythedragonslayer/maglisp + + + + + conferencePaper + + + + Magic: The Gathering in Common Lisp + + + https://vixra.org/abs/2001.0065 + + + + + conferencePaper + + + + The Complexity of Deciding Legality of a Single Step of Magic: The Gathering + + + https://livrepository.liverpool.ac.uk/3029568/ + + + + + bookSection + + + 11302 + ISBN 978-3-030-04178-6 978-3-030-04179-3 + Neural Information Processing + + + + + + + Cham + + + Springer International Publishing + + + + + + + Cheng + Long + + + + + Leung + Andrew Chi Sing + + + + + Ozawa + Seiichi + + + + + + + + + Zilio + Felipe + + + + + Prates + Marcelo + + + + + Lamb + Luis + + + + + + Neural Networks Models for Analyzing Magic: The Gathering Cards + 2018 + Neural Networks Models for Analyzing Magic + DOI.org (Crossref) + + + http://link.springer.com/10.1007/978-3-030-04179-3_20 + + + 2021-06-28 15:33:26 + Series Title: Lecture Notes in Computer Science +DOI: 10.1007/978-3-030-04179-3_20 + 227-239 + + + attachment + Submitted Version + + + https://arxiv.org/pdf/1810.03744 + + + 2021-06-28 15:33:36 + 1 + application/pdf + + + journalArticle + + arXiv:0804.0071 [math] + + + + + + Yao + Erlin + + + + + + + + 65C20 + + + 91-01 + + + + Mathematics - Probability + + + A Theoretical Study of Mafia Games + Mafia can be described as an experiment in human psychology and mass hysteria, or as a game between informed minority and uninformed majority. Focus on a very restricted setting, Mossel et al. [to appear in Ann. Appl. Probab. Volume 18, Number 2] showed that in the mafia game without detectives, if the civilians and mafias both adopt the optimal randomized strategy, then the two groups have comparable probabilities of winning exactly when the total player size is R and the mafia size is of order Sqrt(R). They also proposed a conjecture which stated that this phenomenon should be valid in a more extensive framework. In this paper, we first indicate that the main theorem given by Mossel et al. [to appear in Ann. Appl. Probab. Volume 18, Number 2] can not guarantee their conclusion, i.e., the two groups have comparable winning probabilities when the mafia size is of order Sqrt(R). Then we give a theorem which validates the correctness of their conclusion. In the last, by proving the conjecture proposed by Mossel et al. [to appear in Ann. Appl. Probab. Volume 18, Number 2], we generalize the phenomenon to a more extensive framework, of which the mafia game without detectives is only a special case. + 2008-04-01 + arXiv.org + + + http://arxiv.org/abs/0804.0071 + + + 2021-06-28 15:33:04 + arXiv: 0804.0071 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/0804.0071 + + + 2021-06-28 15:33:10 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/0804.0071.pdf + + + 2021-06-28 15:33:07 + 1 + application/pdf + + + bookSection + + + 10068 + ISBN 978-3-319-50934-1 978-3-319-50935-8 + Computers and Games + + + + + + + Cham + + + Springer International Publishing + + + + + + + Plaat + Aske + + + + + Kosters + Walter + + + + + van den Herik + Jaap + + + + + + + + + Bi + Xiaoheng + + + + + Tanaka + Tetsuro + + + + + + Human-Side Strategies in the Werewolf Game Against the Stealth Werewolf Strategy + 2016 + DOI.org (Crossref) + + + http://link.springer.com/10.1007/978-3-319-50935-8_9 + + + 2021-06-28 15:32:54 + Series Title: Lecture Notes in Computer Science +DOI: 10.1007/978-3-319-50935-8_9 + 93-102 + + + attachment + Full Text + + + https://sci-hub.se/downloads/2019-01-26//f7/bi2016.pdf#view=FitH + + + 2021-06-28 15:33:08 + 1 + application/pdf + + + journalArticle + + arXiv:1905.08617 [cs] + + + + + + Bai + Chongyang + + + + + Bolonkin + Maksim + + + + + Burgoon + Judee + + + + + Chen + Chao + + + + + Dunbar + Norah + + + + + Singh + Bharat + + + + + Subrahmanian + V. S. + + + + + Wu + Zhe + + + + + + + + + Computer Science - Artificial Intelligence + + + + + Computer Science - Computer Vision and Pattern Recognition + + + Automatic Long-Term Deception Detection in Group Interaction Videos + Most work on automated deception detection (ADD) in video has two restrictions: (i) it focuses on a video of one person, and (ii) it focuses on a single act of deception in a one or two minute video. In this paper, we propose a new ADD framework which captures long term deception in a group setting. We study deception in the well-known Resistance game (like Mafia and Werewolf) which consists of 5-8 players of whom 2-3 are spies. Spies are deceptive throughout the game (typically 30-65 minutes) to keep their identity hidden. We develop an ensemble predictive model to identify spies in Resistance videos. We show that features from low-level and high-level video analysis are insufficient, but when combined with a new class of features that we call LiarRank, produce the best results. We achieve AUCs of over 0.70 in a fully automated setting. Our demo can be found at http://home.cs.dartmouth.edu/~mbolonkin/scan/demo/ + 2019-06-15 + arXiv.org + + + http://arxiv.org/abs/1905.08617 + + + 2021-06-28 15:32:49 + arXiv: 1905.08617 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/1905.08617 + + + 2021-06-28 15:32:58 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/1905.08617.pdf + + + 2021-06-28 15:32:54 + 1 + application/pdf + + + journalArticle + + arXiv:1708.01503 [math] + + + + + + Akiyama + Rika + + + + + Abe + Nozomi + + + + + Fujita + Hajime + + + + + Inaba + Yukie + + + + + Hataoka + Mari + + + + + Ito + Shiori + + + + + Seita + Satomi + + + + + + + + + 55A20 (Primary), 05A99 (Secondary) + + + + + Mathematics - Combinatorics + + + + + Mathematics - Geometric Topology + + + + + Mathematics - History and Overview + + + Maximum genus of the Jenga like configurations + We treat the boundary of the union of blocks in the Jenga game as a surface with a polyhedral structure and consider its genus. We generalize the game and determine the maximum genus of the generalized game. + 2018-08-31 + arXiv.org + + + http://arxiv.org/abs/1708.01503 + + + 2021-06-28 15:28:12 + arXiv: 1708.01503 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/1708.01503 + + + 2021-06-28 15:28:24 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/1708.01503.pdf + + + 2021-06-28 15:28:21 + 1 + application/pdf + + + journalArticle + + arXiv:2010.02923 [cs] + + + + + + Gray + Jonathan + + + + + Lerer + Adam + + + + + Bakhtin + Anton + + + + + Brown + Noam + + + + + + + + + Computer Science - Artificial Intelligence + + + + + Computer Science - Machine Learning + + + + + Computer Science - Computer Science and Game Theory + + + Human-Level Performance in No-Press Diplomacy via Equilibrium Search + Prior AI breakthroughs in complex games have focused on either the purely adversarial or purely cooperative settings. In contrast, Diplomacy is a game of shifting alliances that involves both cooperation and competition. For this reason, Diplomacy has proven to be a formidable research challenge. In this paper we describe an agent for the no-press variant of Diplomacy that combines supervised learning on human data with one-step lookahead search via regret minimization. Regret minimization techniques have been behind previous AI successes in adversarial games, most notably poker, but have not previously been shown to be successful in large-scale games involving cooperation. We show that our agent greatly exceeds the performance of past no-press Diplomacy bots, is unexploitable by expert humans, and ranks in the top 2% of human players when playing anonymous games on a popular Diplomacy website. + 2021-05-03 + arXiv.org + + + http://arxiv.org/abs/2010.02923 + + + 2021-06-28 15:28:02 + arXiv: 2010.02923 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/2010.02923 + + + 2021-06-28 15:28:22 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/2010.02923.pdf + + + 2021-06-28 15:28:18 + 1 + application/pdf + + + conferencePaper + + + + Hanabi Open Agent Dataset + + + https://dl.acm.org/doi/10.5555/3463952.3464188 + + + + + computerProgram + Hanabi Open Agent Dataset + + + https://github.com/aronsar/hoad + + + + + journalArticle + + arXiv:1811.11273 [cs] + + + + + + Bendekgey + Henry + + + + + + + + + Computer Science - Artificial Intelligence + + + Clustering Player Strategies from Variable-Length Game Logs in Dominion + We present a method for encoding game logs as numeric features in the card game Dominion. We then run the manifold learning algorithm t-SNE on these encodings to visualize the landscape of player strategies. By quantifying game states as the relative prevalence of cards in a player's deck, we create visualizations that capture qualitative differences in player strategies. Different ways of deviating from the starting game state appear as different rays in the visualization, giving it an intuitive explanation. This is a promising new direction for understanding player strategies across games that vary in length. + 2018-12-12 + arXiv.org + + + http://arxiv.org/abs/1811.11273 + + + 2021-06-28 14:43:21 + arXiv: 1811.11273 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/1811.11273 + + + 2021-06-28 14:43:31 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/1811.11273.pdf + + + 2021-06-28 14:43:27 + 1 + application/pdf + + + forumPost + + + + Dominion Strategy Forum + + + http://forum.dominionstrategy.com/index.php + + + + + blogPost + + + + Card Winning Stats on Dominion Server + + + http://councilroom.com/supply_win + + + + + blogPost + + + + Optimal Card Ratios in Dominion + + + http://councilroom.com/optimal_card_ratios + + + + + blogPost + + + + Best and worst openings in Dominion + + + http://councilroom.com/openings + + + + + computerProgram + Dominion Simulator Source Code + + + https://github.com/mikemccllstr/dominionstats/ + + + + + computerProgram + Dominion Simulator + + + https://dominionsimulator.wordpress.com/f-a-q/ + + + + + journalArticle + + + The World Wide Web Conference + DOI 10.1145/3308558.3314131 + + + + + + + Hsu + Chao-Chun + + + + + Chen + Yu-Hua + + + + + Chen + Zi-Yuan + + + + + Lin + Hsin-Yu + + + + + Huang + Ting-Hao 'Kenneth' + + + + + Ku + Lun-Wei + + + + + + + + + Computer Science - Computation and Language + + + Dixit: Interactive Visual Storytelling via Term Manipulation + In this paper, we introduce Dixit, an interactive visual storytelling system that the user interacts with iteratively to compose a short story for a photo sequence. The user initiates the process by uploading a sequence of photos. Dixit first extracts text terms from each photo which describe the objects (e.g., boy, bike) or actions (e.g., sleep) in the photo, and then allows the user to add new terms or remove existing terms. Dixit then generates a short story based on these terms. Behind the scenes, Dixit uses an LSTM-based model trained on image caption data and FrameNet to distill terms from each image and utilizes a transformer decoder to compose a context-coherent story. Users change images or terms iteratively with Dixit to create the most ideal story. Dixit also allows users to manually edit and rate stories. The proposed procedure opens up possibilities for interpretable and controllable visual storytelling, allowing users to understand the story formation rationale and to intervene in the generation process. + 2019-05-13 + Dixit + arXiv.org + + + http://arxiv.org/abs/1903.02230 + + + 2021-06-28 14:40:29 + arXiv: 1903.02230 + 3531-3535 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/1903.02230 + + + 2021-06-28 14:40:43 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/1903.02230.pdf + + + 2021-06-28 14:40:38 + 1 + application/pdf + + + conferencePaper + + + ISBN 978-1-4799-0565-2 978-1-4799-0562-1 978-1-4799-0563-8 + The 17th CSI International Symposium on Computer Architecture & Digital Systems (CADS 2013) + DOI 10.1109/CADS.2013.6714256 + + + + + + + Tehran, Iran + + + IEEE + + + + + + + Jahanshahi + Ali + + + + + Taram + Mohammad Kazem + + + + + Eskandari + Nariman + + + + + + Blokus Duo game on FPGA + 10/2013 + DOI.org (Crossref) + + + http://ieeexplore.ieee.org/document/6714256/ + + + 2021-06-28 14:39:04 + 149-152 + + + 2013 17th CSI International Symposium on Computer Architecture and Digital Systems (CADS) + + + + + attachment + Full Text + + + https://zero.sci-hub.se/3228/9ae6ca1efab5a2ebb63dd4e22a13bf04/jahanshahi2013.pdf#view=FitH + + + 2021-06-28 14:39:07 + 1 + application/pdf + + + conferencePaper + + + ISBN 978-1-4799-2198-0 978-1-4799-2199-7 + 2013 International Conference on Field-Programmable Technology (FPT) + DOI 10.1109/FPT.2013.6718426 + + + + + + + Kyoto, Japan + + + IEEE + + + + + + + Yoza + Takashi + + + + + Moriwaki + Retsu + + + + + Torigai + Yuki + + + + + Kamikubo + Yuki + + + + + Kubota + Takayuki + + + + + Watanabe + Takahiro + + + + + Fujimori + Takumi + + + + + Ito + Hiroyuki + + + + + Seo + Masato + + + + + Akagi + Kouta + + + + + Yamaji + Yuichiro + + + + + Watanabe + Minoru + + + + + + FPGA Blokus Duo Solver using a massively parallel architecture + 12/2013 + DOI.org (Crossref) + + + http://ieeexplore.ieee.org/document/6718426/ + + + 2021-06-28 14:38:57 + 494-497 + + + 2013 International Conference on Field-Programmable Technology (FPT) + + + + + attachment + Full Text + + + https://zero.sci-hub.se/2654/a4d3e713290066b6db7db1d9eedd194e/yoza2013.pdf#view=FitH + + + 2021-06-28 14:39:08 + 1 + application/pdf + + + report + Blokus Game Solver + + + https://digitalcommons.calpoly.edu/cpesp/290/ + + + + + computerProgram + Ceramic: A research environment based on the multi-player strategic board game Azul + + + https://github.com/Swynfel/ceramic + + + + + conferencePaper + + + + Ceramic: A research environment based on the multi-player strategic board game Azul + + + https://ipsj.ixsq.nii.ac.jp/ej/?action=repository_action_common_download&item_id=207669&item_no=1&attribute_id=1&file_no=1 + + + + + report + A summary of a dissertation on Azul + + + https://old.reddit.com/r/boardgames/comments/hxodaf/update_i_wrote_my_dissertation_on_azul/ + + + + + conferencePaper + + + + + + + + Pfeiffer + Michael + + + + + + Reinforcement Learning of Strategies for Settlers of Catan + 2004 + + + https://www.researchgate.net/publication/228728063_Reinforcement_learning_of_strategies_for_Settlers_of_Catan + + + + + attachment + Pfeiffer - 2004 - Reinforcement Learning of Strategies for Settlers .pdf + application/pdf + + + conferencePaper + + + 34 + Proceedings of the Annual Meeting of the Cognitive Science Society + + + + + + + Guhe + Markus + + + + + Lascarides + Alex + + + + + + Trading in a multiplayer board game: Towards an analysis of non-cooperative dialogue + 2012 + + + https://escholarship.org/uc/item/9zt506xx + + + Issue: 34 + + + attachment + Guhe and Lascarides - 2012 - Trading in a multiplayer board game Towards an an.pdf + application/pdf + + + conferencePaper + + + + + Applying Neural Networks and Genetic Programming to the Game Lost Cities + + + https://minds.wisconsin.edu/bitstream/handle/1793/79080/LydeenSpr18.pdf?sequence=1&isAllowed=y + + + + + attachment + LydeenSpr18.pdf + + + https://minds.wisconsin.edu/bitstream/handle/1793/79080/LydeenSpr18.pdf + + + 2021-06-12 17:03:24 + 3 + + + journalArticle + + arXiv:1511.08099 [cs] + + + + + + Cuayáhuitl + Heriberto + + + + + Keizer + Simon + + + + + Lemon + Oliver + + + + + + + + + Computer Science - Artificial Intelligence + + + + + Computer Science - Machine Learning + + + Strategic Dialogue Management via Deep Reinforcement Learning + Artificially intelligent agents equipped with strategic skills that can negotiate during their interactions with other natural or artificial agents are still underdeveloped. This paper describes a successful application of Deep Reinforcement Learning (DRL) for training intelligent agents with strategic conversational skills, in a situated dialogue setting. Previous studies have modelled the behaviour of strategic agents using supervised learning and traditional reinforcement learning techniques, the latter using tabular representations or learning with linear function approximation. In this study, we apply DRL with a high-dimensional state space to the strategic board game of Settlers of Catan---where players can offer resources in exchange for others and they can also reply to offers made by other players. Our experimental results report that the DRL-based learnt policies significantly outperformed several baselines including random, rule-based, and supervised-based behaviours. The DRL-based policy has a 53% win rate versus 3 automated players (`bots'), whereas a supervised player trained on a dialogue corpus in this setting achieved only 27%, versus the same 3 bots. This result supports the claim that DRL is a promising framework for training dialogue systems, and strategic agents with negotiation abilities. + 2015-11-25 + arXiv.org + + + http://arxiv.org/abs/1511.08099 + + + 2021-01-02 18:29:38 + arXiv: 1511.08099 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/1511.08099 + + + 2021-01-02 18:29:50 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/1511.08099.pdf + + + 2021-01-02 18:29:43 + 1 + application/pdf + + + journalArticle + + arXiv:2004.00377 [cs] + + + + + + Muller-Brockhausen + Matthias + + + + + Preuss + Mike + + + + + Plaat + Aske + + + + + + + + + Computer Science - Artificial Intelligence + + + A New Challenge: Approaching Tetris Link with AI + Decades of research have been invested in making computer programs for playing games such as Chess and Go. This paper focuses on a new game, Tetris Link, a board game that is still lacking any scientific analysis. Tetris Link has a large branching factor, hampering a traditional heuristic planning approach. We explore heuristic planning and two other approaches: Reinforcement Learning, Monte Carlo tree search. We document our approach and report on their relative performance in a tournament. Curiously, the heuristic approach is stronger than the planning/learning approaches. However, experienced human players easily win the majority of the matches against the heuristic planning AIs. We, therefore, surmise that Tetris Link is more difficult than expected. We offer our findings to the community as a challenge to improve upon. + 2020-04-01 + A New Challenge + arXiv.org + + + http://arxiv.org/abs/2004.00377 + + + 2021-01-02 18:18:26 + arXiv: 2004.00377 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/2004.00377 + + + 2021-01-02 18:18:38 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/2004.00377.pdf + + + 2021-01-02 18:18:32 + 1 + application/pdf + + + journalArticle + + arXiv:2006.02353 [cs] + + + + + + Bertholon + Guillaume + + + + + Géraud-Stewart + Rémi + + + + + Kugelmann + Axel + + + + + Lenoir + Théo + + + + + Naccache + David + + + + + + + + + Computer Science - Computer Science and Game Theory + + + At Most 43 Moves, At Least 29: Optimal Strategies and Bounds for Ultimate Tic-Tac-Toe + Ultimate Tic-Tac-Toe is a variant of the well known tic-tac-toe (noughts and crosses) board game. Two players compete to win three aligned "fields", each of them being a tic-tac-toe game. Each move determines which field the next player must play in. We show that there exist a winning strategy for the first player, and therefore that there exist an optimal winning strategy taking at most 43 moves; that the second player can hold on at least 29 rounds; and identify any optimal strategy's first two moves. + 2020-06-06 + At Most 43 Moves, At Least 29 + arXiv.org + + + http://arxiv.org/abs/2006.02353 + + + 2021-01-02 18:17:55 + arXiv: 2006.02353 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/2006.02353 + + + 2021-01-02 18:18:02 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/2006.02353.pdf + + + 2021-01-02 18:17:57 + 1 + application/pdf + + + journalArticle + + arXiv:2007.15895 [cs] + + + + + + Tanaka + Satoshi + + + + + Bonnet + François + + + + + Tixeuil + Sébastien + + + + + Tamura + Yasumasa + + + + + + + + + Computer Science - Computer Science and Game Theory + + + Quixo Is Solved + Quixo is a two-player game played on a 5$\times$5 grid where the players try to align five identical symbols. Specifics of the game require the usage of novel techniques. Using a combination of value iteration and backward induction, we propose the first complete analysis of the game. We describe memory-efficient data structures and algorithmic optimizations that make the game solvable within reasonable time and space constraints. Our main conclusion is that Quixo is a Draw game. The paper also contains the analysis of smaller boards and presents some interesting states extracted from our computations. + 2020-07-31 + arXiv.org + + + http://arxiv.org/abs/2007.15895 + + + 2021-01-02 18:17:10 + arXiv: 2007.15895 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/2007.15895 + + + 2021-01-02 18:17:21 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/2007.15895.pdf + + + 2021-01-02 18:17:17 + 1 + application/pdf + + + journalArticle + + arXiv:2009.12974 [cs] + + + + + + Ameneyro + Fred Valdez + + + + + Galvan + Edgar + + + + + Morales + Anger Fernando Kuri + + + + + + + + + Computer Science - Artificial Intelligence + + + Playing Carcassonne with Monte Carlo Tree Search + Monte Carlo Tree Search (MCTS) is a relatively new sampling method with multiple variants in the literature. They can be applied to a wide variety of challenging domains including board games, video games, and energy-based problems to mention a few. In this work, we explore the use of the vanilla MCTS and the MCTS with Rapid Action Value Estimation (MCTS-RAVE) in the game of Carcassonne, a stochastic game with a deceptive scoring system where limited research has been conducted. We compare the strengths of the MCTS-based methods with the Star2.5 algorithm, previously reported to yield competitive results in the game of Carcassonne when a domain-specific heuristic is used to evaluate the game states. We analyse the particularities of the strategies adopted by the algorithms when they share a common reward system. The MCTS-based methods consistently outperformed the Star2.5 algorithm given their ability to find and follow long-term strategies, with the vanilla MCTS exhibiting a more robust game-play than the MCTS-RAVE. + 2020-10-04 + arXiv.org + + + http://arxiv.org/abs/2009.12974 + + + 2021-01-02 18:13:09 + arXiv: 2009.12974 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/2009.12974 + + + 2021-01-02 18:13:17 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/2009.12974.pdf + + + 2021-01-02 18:13:12 + 1 + application/pdf + + + journalArticle + + arXiv:2005.07156 [cs] + + + + + + Reinhardt + Jack + + + + + + + + + Computer Science - Artificial Intelligence + + + + + Computer Science - Multiagent Systems + + + Competing in a Complex Hidden Role Game with Information Set Monte Carlo Tree Search + Advances in intelligent game playing agents have led to successes in perfect information games like Go and imperfect information games like Poker. The Information Set Monte Carlo Tree Search (ISMCTS) family of algorithms outperforms previous algorithms using Monte Carlo methods in imperfect information games. In this paper, Single Observer Information Set Monte Carlo Tree Search (SO-ISMCTS) is applied to Secret Hitler, a popular social deduction board game that combines traditional hidden role mechanics with the randomness of a card deck. This combination leads to a more complex information model than the hidden role and card deck mechanics alone. It is shown in 10108 simulated games that SO-ISMCTS plays as well as simpler rule based agents, and demonstrates the potential of ISMCTS algorithms in complicated information set domains. + 2020-05-14 + arXiv.org + + + http://arxiv.org/abs/2005.07156 + + + 2020-11-26 09:00:33 + arXiv: 2005.07156 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/2005.07156 + + + 2020-11-26 09:01:10 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/2005.07156.pdf + + + 2020-11-26 09:01:03 + 1 + application/pdf + + + conferencePaper + + + 2020第82回全国大会講演論文集 + + + + + + + ひい + とう + + + + + 市来 + 正裕 + + + + + 中里 + 研一 + + + + + + Playing mini-Hanabi card game with Q-learning + February 2020 + + + http://id.nii.ac.jp/1001/00205046/ + + + Issue: 1 + 41–42 + + + attachment + View PDF + + + https://ipsj.ixsq.nii.ac.jp/ej/?action=repository_uri&item_id=205142&file_id=1&file_no=1 + + + 2020-11-26 08:54:47 + 3 + + + journalArticle + + + 16 + Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment + 1 + AIIDE + + + + + + + Canaan + Rodrigo + + + + + Gao + Xianbo + + + + + Chung + Youjin + + + + + Togelius + Julian + + + + + Nealen + Andy + + + + + Menzel + Stefan + + + + + + Behavioral Evaluation of Hanabi Rainbow DQN Agents and Rule-Based Agents + <p class="abstract">Hanabi is a multiplayer cooperative card game, where only your partners know your cards. All players succeed or fail together. This makes the game an excellent testbed for studying collaboration. Recently, it has been shown that deep neural networks can be trained through self-play to play the game very well. However, such agents generally do not play well with others. In this paper, we investigate the consequences of training Rainbow DQN agents with human-inspired rule-based agents. We analyze with which agents Rainbow agents learn to play well, and how well playing skill transfers to agents they were not trained with. We also analyze patterns of communication between agents to elucidate how collaboration happens. A key finding is that while most agents only learn to play well with partners seen during training, one particular agent leads the Rainbow algorithm towards a much more general policy. The metrics and hypotheses advanced in this paper can be used for further study of collaborative agents.</p> + October 1, 2020 + + + https://ojs.aaai.org/index.php/AIIDE/article/view/7404 + + + 2020-11-26 + Section: Full Oral Papers + 31-37 + + + attachment + View PDF + + + https://ojs.aaai.org/index.php/AIIDE/article/view/7404/7333 + + + 2020-11-26 08:52:38 + 3 + + + journalArticle + + + + + + Eger + Markus + + + + + Martens + Chris + + + + + Sauma Chacon + Pablo + + + + + Alfaro Cordoba + Marcela + + + + + Hidalgo Cespedes + Jeisson + + + + + + Operationalizing Intentionality to Play Hanabi with Human Players + 2020 + DOI.org (Crossref) + + + https://ieeexplore.ieee.org/document/9140404/ + + + 2020-11-26 08:48:44 + 1-1 + + + IEEE Transactions on Games + DOI 10.1109/TG.2020.3009359 + IEEE Trans. Games + ISSN 2475-1502, 2475-1510 + + + attachment + Full Text + + + https://sci-hub.se/downloads/2020-08-17/f1/eger2020.pdf?rand=5fbf6bef76c6b#view=FitH + + + 2020-11-26 08:48:52 + 1 + application/pdf + + + conferencePaper + + + ISBN 978-1-4503-8878-8 + 11th Hellenic Conference on Artificial Intelligence + DOI 10.1145/3411408.3411413 + + + + + + + Athens Greece + + + ACM + + + + + + + Theodoridis + Alexios + + + + + Chalkiadakis + Georgios + + + + + Monte Carlo Tree Search for the Game of Diplomacy + 2020-09-02 + en + DOI.org (Crossref) + + + https://dl.acm.org/doi/10.1145/3411408.3411413 + + + 2020-10-12 04:20:38 + 16-25 + + + SETN 2020: 11th Hellenic Conference on Artificial Intelligence + + + + + journalArticle + + + arXiv:2008.07079 [cs, stat] + + + + + + + Gendre + Quentin + + + + + Kaneko + Tomoyuki + + + + + + + + + Computer Science - Artificial Intelligence + + + + + Computer Science - Machine Learning + + + + + Statistics - Machine Learning + + + Playing Catan with Cross-dimensional Neural Network + Catan is a strategic board game having interesting properties, including multi-player, imperfect information, stochastic, complex state space structure (hexagonal board where each vertex, edge and face has its own features, cards for each player, etc), and a large action space (including negotiation). Therefore, it is challenging to build AI agents by Reinforcement Learning (RL for short), without domain knowledge nor heuristics. In this paper, we introduce cross-dimensional neural networks to handle a mixture of information sources and a wide variety of outputs, and empirically demonstrate that the network dramatically improves RL in Catan. We also show that, for the first time, a RL agent can outperform jsettler, the best heuristic agent available. + 2020-08-17 + arXiv.org + + + http://arxiv.org/abs/2008.07079 + + + 2020-10-12 04:19:57 + arXiv: 2008.07079 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/2008.07079 + + + 2020-10-12 04:20:10 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/2008.07079.pdf + + + 2020-10-12 04:20:04 + 1 + application/pdf + + + conferencePaper + + + ISBN 978-1-4503-6571-0 + Proceedings of the 13th International Conference on the Foundations of Digital Games + DOI 10.1145/3235765.3235813 + + + + + + + Malmö Sweden + + + ACM + + + + + + + de Mesentier Silva + Fernando + + + + + Lee + Scott + + + + + Togelius + Julian + + + + + Nealen + Andy + + + + + + Evolving maps and decks for ticket to ride + 2018-08-07 + en + DOI.org (Crossref) + + + https://dl.acm.org/doi/10.1145/3235765.3235813 + + + 2020-10-12 04:12:33 + 1-7 + + + FDG '18: Foundations of Digital Games 2018 + + + + + attachment + Full Text + + + https://twin.sci-hub.se/7128/24e28b0429626f565aafd93768332e73/demesentiersilva2018.pdf#view=FitH + + + 2020-10-12 04:12:36 + 1 + application/pdf + + + computerProgram + + + + + Copley + Rowan + + + + + Materials for Ticket to Ride Seattle and a framework for making more game boards + + + https://github.com/dovinmu/ttr_generator + + + + + conferencePaper + + + ISBN 978-1-4503-5319-9 + Proceedings of the International Conference on the Foundations of Digital Games - FDG '17 + DOI 10.1145/3102071.3102105 + + + + + + + Hyannis, Massachusetts + + + ACM Press + + + + + + + de Mesentier Silva + Fernando + + + + + Lee + Scott + + + + + Togelius + Julian + + + + + Nealen + Andy + + + + + + + AI-based playtesting of contemporary board games + 2017 + en + DOI.org (Crossref) + + + http://dl.acm.org/citation.cfm?doid=3102071.3102105 + + + 2020-10-12 04:09:30 + 1-10 + + + the International Conference + + + + + attachment + PDF + + + http://game.engineering.nyu.edu/wp-content/uploads/2017/06/ticket-ride-fdg2017-camera-ready.pdf + + + 2020-10-12 04:13:00 + 3 + + + attachment + Full Text + + + https://twin.sci-hub.se/6553/d80b9cdf7f993e1137d0b129dec94e6d/demesentiersilva2017.pdf#view=FitH + + + 2020-10-12 04:09:38 + 1 + application/pdf + + + webpage + + + + Shobu randomly played games dataset + + + https://www.kaggle.com/bsfoltz/shobu-randomly-played-games-104k + + + + + computerProgram + Shobu AI Playground + + + https://github.com/JayWalker512/Shobu + + + + + journalArticle + + arXiv:2010.00048 [cs] + + + + + + Kunda + Maithilee + + + + + Rabkina + Irina + + + + + + + + + Computer Science - Artificial Intelligence + + + Creative Captioning: An AI Grand Challenge Based on the Dixit Board Game + We propose a new class of "grand challenge" AI problems that we call creative captioning---generating clever, interesting, or abstract captions for images, as well as understanding such captions. Creative captioning draws on core AI research areas of vision, natural language processing, narrative reasoning, and social reasoning, and across all these areas, it requires sophisticated uses of common sense and cultural knowledge. In this paper, we analyze several specific research problems that fall under creative captioning, using the popular board game Dixit as both inspiration and proposed testing ground. We expect that Dixit could serve as an engaging and motivating benchmark for creative captioning across numerous AI research communities for the coming 1-2 decades. + 2020-09-30 + Creative Captioning + arXiv.org + + + http://arxiv.org/abs/2010.00048 + + + 2020-10-12 04:03:28 + arXiv: 2010.00048 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/2010.00048 + + + 2020-10-12 04:03:53 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/2010.00048.pdf + + + 2020-10-12 04:03:46 + 1 + application/pdf + + + thesis + + + Utrecht University + + + + + + + Andel + Daniël + + + + + + On the complexity of Hive + It is shown that for an arbitrary position of a Hive game where both players have the same set of N pieces it is PSPACE-hard to determine whether one of the players has a winning strategy. The proof is done by reducing the known PSPACE-complete set of true quantified boolean formulas to a game concerning these formulas, then to the game generalised geography, then to a version of that game with the restriction of having only nodes with maximum degree 3, and finally to generalised Hive. This thesis includes a short introduction to the subject of computational complexity. + May 2020 + en-US + On the complexity of Hive + + + https://dspace.library.uu.nl/handle/1874/396955 + + + 33 + Bachelor thesis + + + attachment + Andel - 2020 - On the complexity of Hive.pdf + application/pdf + + + journalArticle + + + + + + Heron + Michael James + + + + + Belford + Pauline Helen + + + + + Reid + Hayley + + + + + Crabb + Michael + + + + + + Eighteen Months of Meeple Like Us: An Exploration into the State of Board Game Accessibility + 6/2018 + en + Eighteen Months of Meeple Like Us + DOI.org (Crossref) + + + http://link.springer.com/10.1007/s40869-018-0056-9 + + + 2020-07-28 09:09:05 + 75-95 + + + 7 + The Computer Games Journal + DOI 10.1007/s40869-018-0056-9 + 2 + Comput Game J + ISSN 2052-773X + + + attachment + Full Text + + + https://link.springer.com/content/pdf/10.1007/s40869-018-0056-9.pdf + + + 2020-07-28 09:09:08 + 1 + application/pdf + + + journalArticle + + + 7 + The Computer Games Journal + DOI 10.1007/s40869-018-0057-8 + 2 + Comput Game J + ISSN 2052-773X + + + + + + + Heron + Michael James + + + + + Belford + Pauline Helen + + + + + Reid + Hayley + + + + + Crabb + Michael + + + + + + Meeple Centred Design: A Heuristic Toolkit for Evaluating the Accessibility of Tabletop Games + 6/2018 + en + Meeple Centred Design + DOI.org (Crossref) + + + http://link.springer.com/10.1007/s40869-018-0057-8 + + + 2020-07-28 09:08:52 + 97-114 + + + attachment + Full Text + + + https://link.springer.com/content/pdf/10.1007/s40869-018-0057-8.pdf + + + 2020-07-28 09:08:55 + 1 + application/pdf + + + journalArticle + + + + + Ludic Language Pedagogy + + + Ludic Language Pedagogy + + + + + + + deHaan + Jonathan + + + + + + Jidoukan Jenga: Teaching English through remixing games and game rules + Let students play simple games in their L1. It’s ok! + + Then: + + You, the teacher, can help them critique the game in their L2. + You, the teacher, can help them change the game in their L2. + You, the teacher, can help them develop themselves. + + #dropthestick #dropthecarrot #bringmeaning + 2020-04-15 + Teaching English through remixing games and game rules + + + https://www.llpjournal.org/2020/04/13/jidokan-jenga.html + + + 📍 What is this? This is a recollection of a short lesson with some children. I used Jenga and a dictionary. + 📍 Why did you make it? I want to show language teachers that simple games, and playing simple games in students’ first language can be a great foundation for helping students learn new vocabulary, think critically, and exercise creativity. + 📍 Why is it radical? I taught using a simple board game (at a time when video games are over-focused on in research). I show what the learning looks like (I include a photo). The teaching and learning didn’t occur in a laboratory setting, but in the wild (in a community center). I focused on the learning around games. + 📍 Who is it for? Language teachers can easily implement this lesson using Jenga or any other game. Language researchers can expand on the translating and remixing potential around games. + + + attachment + deHaan - 2020 - Jidoukan Jenga Teaching English through remixing .pdf + application/pdf + + + computerProgram + A framework for writing bots that play Hanabi + + + https://github.com/Quuxplusone/Hanabi + + + + + computerProgram + A strategy simulator for the well-known cooperative card game Hanabi + + + https://github.com/rjtobin/HanSim + + + + + computerProgram + State of the art Hanabi bots + simulation framework in rust + + + https://github.com/WuTheFWasThat/hanabi.rs + + + + + journalArticle + + + + + + + + Gottwald + Eva Tallula + + + + + Eger + Markus + + + + + Martens + Chris + + + + + + I see what you see: Integrating eye tracking into Hanabi playing agents + Humans’ eye movements convey a lot of information about their intentions, often unconsciously. Intelligent agents that cooperate with humans in various domains can benefit from interpreting this information. This paper contains a preliminary look at how eye tracking could be useful for agents that play the cooperative card game Hanabi with human players. We outline several situations in which an AI agent can utilize gaze information, and present an outlook on how we plan to integrate this with reimplementations of contemporary Hanabi agents. + en + Zotero + 4 + + + attachment + Gottwald et al. - I see what you see Integrating eye tracking into .pdf + application/pdf + + + journalArticle + + Cape Cod + + + + + + Eger + Markus + + + + + Martens + Chris + + + + + + A Browser-based Interface for the Exploration and Evaluation of Hanabi AIs + 2017 + en + Zotero + + + http://fdg2017.org/papers/FDG2017_demo_Hanabi.pdf + + + 4 + + + attachment + Eger and Martens - 2017 - A Browser-based Interface for the Exploration and .pdf + application/pdf + + + conferencePaper + + + + + + + + Osawa + Hirotaka + + + + + + Solving Hanabi: Estimating Hands by Opponent's Actions in Cooperative Game with Incomplete Information + A unique behavior of humans is modifying one’s unobservable behavior based on the reaction of others for cooperation. We used a card game called Hanabi as an evaluation task of imitating human reflective intelligence with artificial intelligence. Hanabi is a cooperative card game with incomplete information. A player cooperates with an opponent in building several card sets constructed with the same color and ordered numbers. However, like a blind man's bluff, each player sees the cards of all other players except his/her own. Also, communication between players is restricted to information about the same numbers and colors, and the player is required to read his/his opponent's intention with the opponent's hand, estimate his/her cards with incomplete information, and play one of them for building a set. We compared human play with several simulated strategies. The results indicate that the strategy with feedbacks from simulated opponent's viewpoints achieves more score than other strategies. + 2015 + + + https://aaai.org/ocs/index.php/WS/AAAIW15/paper/view/10167 + + + + AAAI Workshops + + + + attachment + Osawa - 2015 - Solving Hanabi Estimating Hands by Opponent's Act.pdf + application/pdf + + + conferencePaper + + + ISBN 978-1-5386-3233-8 + 2017 IEEE Conference on Computational Intelligence and Games (CIG) + DOI 10.1109/CIG.2017.8080417 + + + + + + + New York, NY, USA + + + IEEE + + + + + + + Eger + Markus + + + + + Martens + Chris + + + + + Cordoba + Marcela Alfaro + + + + + + An intentional AI for hanabi + 8/2017 + DOI.org (Crossref) + + + http://ieeexplore.ieee.org/document/8080417/ + + + 2020-07-21 11:03:36 + 68-75 + + + 2017 IEEE Conference on Computational Intelligence and Games (CIG) + + + + + attachment + Full Text + + + https://zero.sci-hub.se/6752/bcf6e994ee7503ab821bd67848727b05/eger2017.pdf#view=FitH + + + 2020-07-21 11:03:40 + 1 + application/pdf + + + bookSection + + + 10664 + ISBN 978-3-319-71648-0 978-3-319-71649-7 + Advances in Computer Games + + + + + + + Cham + + + Springer International Publishing + + + + + + + Winands + Mark H.M. + + + + + van den Herik + H. Jaap + + + + + Kosters + Walter A. + + + + + + + + + Bouzy + Bruno + + + + + Playing Hanabi Near-Optimally + 2017 + DOI.org (Crossref) + + + http://link.springer.com/10.1007/978-3-319-71649-7_5 + + + 2020-07-21 11:02:53 + Series Title: Lecture Notes in Computer Science +DOI: 10.1007/978-3-319-71649-7_5 + 51-62 + + + bookSection + + + 765 + ISBN 978-3-319-67467-4 978-3-319-67468-1 + BNAIC 2016: Artificial Intelligence + + + + + + + Cham + + + Springer International Publishing + + + + + + + Bosse + Tibor + + + + + Bredeweg + Bert + + + + + + + + + van den Bergh + Mark J. H. + + + + + Hommelberg + Anne + + + + + Kosters + Walter A. + + + + + Spieksma + Flora M. + + + + + + Aspects of the Cooperative Card Game Hanabi + 2017 + DOI.org (Crossref) + + + http://link.springer.com/10.1007/978-3-319-67468-1_7 + + + 2020-07-21 11:02:26 + Series Title: Communications in Computer and Information Science +DOI: 10.1007/978-3-319-67468-1_7 + 93-105 + + + attachment + Full Text + + + https://twin.sci-hub.se/6548/49fca9bfed767f739defcd030c004bdb/vandenbergh2017.pdf#view=FitH + + + 2020-07-21 11:02:31 + 1 + application/pdf + + + conferencePaper + + + ISBN 978-1-5386-4359-4 + 2018 IEEE Conference on Computational Intelligence and Games (CIG) + DOI 10.1109/CIG.2018.8490449 + + + + + + + Maastricht + + + IEEE + + + + + + + Canaan + Rodrigo + + + + + Shen + Haotian + + + + + Torrado + Ruben + + + + + Togelius + Julian + + + + + Nealen + Andy + + + + + Menzel + Stefan + + + + + + Evolving Agents for the Hanabi 2018 CIG Competition + 8/2018 + DOI.org (Crossref) + + + https://ieeexplore.ieee.org/document/8490449/ + + + 2020-07-21 11:01:52 + 1-8 + + + 2018 IEEE Conference on Computational Intelligence and Games (CIG) + + + + + attachment + Submitted Version + + + https://arxiv.org/pdf/1809.09764 + + + 2020-07-21 11:01:56 + 1 + application/pdf + + + conferencePaper + + + DOI 10.1109/CIG.2019.8848097 + + + + + + + Goodman + James + + + + + + Re-determinizing MCTS in Hanabi + 08 2019 + 1-8 + + + attachment + Goodman - 2019 - Re-determinizing MCTS in Hanabi.pdf + application/pdf + + + blogPost + + + + Nmbr9 as a Constraint Programming Challenge + + + https://zayenz.se/blog/post/nmbr9-cp2019-abstract/ + + + + + journalArticle + + arXiv:2001.04238 [cs] + + + + + + Lagerkvist + Mikael Zayenz + + + + + + + + + Computer Science - Artificial Intelligence + + + Nmbr9 as a Constraint Programming Challenge + Modern board games are a rich source of interesting and new challenges for combinatorial problems. The game Nmbr9 is a solitaire style puzzle game using polyominoes. The rules of the game are simple to explain, but modelling the game effectively using constraint programming is hard. This abstract presents the game, contributes new generalized variants of the game suitable for benchmarking and testing, and describes a model for the presented variants. The question of the top possible score in the standard game is an open challenge. + 2020-01-13 + arXiv.org + + + http://arxiv.org/abs/2001.04238 + + + 2020-07-21 10:57:58 + arXiv: 2001.04238 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/2001.04238 + + + 2020-07-21 10:58:02 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/2001.04238.pdf + + + 2020-07-21 10:58:01 + 1 + application/pdf + + + presentation + + State Representation and Polyomino Placement for the Game Patchwork + + + https://zayenz.se/papers/Lagerkvist_ModRef_2019_Presentation.pdf + + + + + attachment + Full Text + + + https://zayenz.se/papers/Lagerkvist_ModRef_2019_Presentation.pdf + + + 2020-07-21 10:56:59 + 1 + application/pdf + + + journalArticle + + arXiv:2001.04233 [cs] + + + + + + Lagerkvist + Mikael Zayenz + + + + + + + + + Computer Science - Artificial Intelligence + + + State Representation and Polyomino Placement for the Game Patchwork + Modern board games are a rich source of entertainment for many people, but also contain interesting and challenging structures for game playing research and implementing game playing agents. This paper studies the game Patchwork, a two player strategy game using polyomino tile drafting and placement. The core polyomino placement mechanic is implemented in a constraint model using regular constraints, extending and improving the model in (Lagerkvist, Pesant, 2008) with: explicit rotation handling; optional placements; and new constraints for resource usage. Crucial for implementing good game playing agents is to have great heuristics for guiding the search when faced with large branching factors. This paper divides placing tiles into two parts: a policy used for placing parts and an evaluation used to select among different placements. Policies are designed based on classical packing literature as well as common standard constraint programming heuristics. For evaluation, global propagation guided regret is introduced, choosing placements based on not ruling out later placements. Extensive evaluations are performed, showing the importance of using a good evaluation and that the proposed global propagation guided regret is a very effective guide. + 2020-01-13 + arXiv.org + + + http://arxiv.org/abs/2001.04233 + + + 2020-07-21 10:55:58 + arXiv: 2001.04233 + + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/2001.04233 + + + 2020-07-21 10:56:13 + 1 + text/html + + + attachment + arXiv Fulltext PDF + + + https://arxiv.org/pdf/2001.04233.pdf + + + 2020-07-21 10:56:09 + 1 + application/pdf + + + blogPost + + + + State Representation and Polyomino Placement for the Game Patchwork + + + https://zayenz.se/blog/post/patchwork-modref2019-paper/ + + + + + journalArticle + + + + + RISK Board Game ‐ Battle Outcome Analysis + + + http://www.c4i.gr/xgeorgio/docs/RISK-board-game%20_rev-3.pdf + + + + + attachment + Full Text + + + https://www.c4i.gr/xgeorgio/docs/RISK-board-game%20_rev-3.pdf + + + 2020-07-20 18:54:23 + 1 + application/pdf + + + journalArticle + + + + + RISKy Business: An In-Depth Look at the Game RISK + + + https://scholar.rose-hulman.edu/rhumj/vol3/iss2/3 + + + + + attachment + RISKy Business An In-Depth Look at the Game RISK.pdf + application/pdf + + + journalArticle + + + + + Monte Carlo Tree Search in a Modern Board Game Framework + + + https://project.dke.maastrichtuniversity.nl/games/files/bsc/Roelofs_Bsc-paper.pdf + + + + + attachment + Full Text + + + https://project.dke.maastrichtuniversity.nl/games/files/bsc/Roelofs_Bsc-paper.pdf + + + 2020-07-20 18:47:19 + 1 + application/pdf + + + blogPost + + + + The impact of loaded dice in Catan + + + https://izbicki.me/blog/how-to-cheat-at-settlers-of-catan-by-loading-the-dice-and-prove-it-with-p-values.html + + + + + journalArticle + + + + + POMCP with Human Preferencesin Settlers of Catan + + + https://www.aaai.org/ocs/index.php/AIIDE/AIIDE18/paper/viewFile/18091/17217 + + + + + attachment + POMCP with Human Preferencesin Settlers of Catan.pdf + application/pdf + + + computerProgram + Settlers of Catan bot trained using reinforcement learning + + + https://jonzia.github.io/Catan/ + + + MATLAB + + + journalArticle + + arxiv:1810.03744 + + + + + + Zilio + Felipe + + + + + Prates + Marcelo + + + + + + Neural Networks Models for Analyzing Magic: the Gathering Cards + 2018 + + + http://arxiv.org/abs/1810.03744v1 + + + + + attachment + Full Text + + + https://arxiv.org/pdf/1810.03744v1.pdf + + + 2020-07-20 18:30:42 1 application/pdf @@ -793,7 +6598,7 @@ - + The Hanabi challenge: A new frontier for AI research March 2020 @@ -804,7 +6609,7 @@ Publisher: Elsevier BV 103216 - + attachment Full Text @@ -816,327 +6621,239 @@ 1 application/pdf - - conferencePaper - - - 2019 IEEE Conference on Games (CoG) - DOI 10.1109/cig.2019.8848008 - - - - IEEE - - - - - - Walton-Rivers - Joseph - - - - - Williams - Piers R. - - - - - Bartle - Richard - - - - - - The 2018 Hanabi competition - August 2019 - - - https://doi.org/10.1109%2Fcig.2019.8848008 - - - - - attachment - Accepted Version - - - https://repository.essex.ac.uk/26898/2/hanabi.pdf - - - 2020-07-20 18:34:35 - 1 - application/pdf - - - conferencePaper - - - 2019 IEEE Conference on Games (CoG) - DOI 10.1109/cig.2019.8847944 - - - - IEEE - - - - - - Canaan - Rodrigo - - - - - Togelius - Julian - - - - - Nealen - Andy - - - - - Menzel - Stefan - - - - - - Diverse Agents for Ad-Hoc Cooperation in Hanabi - August 2019 - - - https://doi.org/10.1109%2Fcig.2019.8847944 - - - - - attachment - Submitted Version - - - https://arxiv.org/pdf/1907.03840 - - - 2020-07-20 18:11:10 - 1 - application/pdf - - + journalArticle - - 45 - Mathematics Magazine - DOI 10.1080/0025570x.1972.11976187 - 1 - + arxiv:1606.07374 - Ash - Robert B. + Yeh + Kun-Hao - Bishop - Richard L. + Wu + I.-Chen + + + + + Hsueh + Chu-Hsuan + + + + + Chang + Chia-Chuan + + + + + Liang + Chao-Chin + + + + + Chiang + Han - - Monopoly as a Markov Process - January 1972 + + Multi-Stage Temporal Difference Learning for 2048-like Games + 2016 - https://doi.org/10.1080%2F0025570x.1972.11976187 + http://arxiv.org/abs/1606.07374v2 - Publisher: Informa UK Limited - 26–29 - - attachment - Submitted Version - - - https://www.math.uiuc.edu/%7Ebishop/monopoly.pdf - - - 2020-07-20 18:37:15 - 1 - application/pdf - - - journalArticle - - - 4 - IEEE Trans. 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I. + Eppstein + David - - Monte Carlo search applied to card selection in Magic: The Gathering - September 2009 + + Making Change in 2048 + 2018 - https://doi.org/10.1109%2Fcig.2009.5286501 + http://arxiv.org/abs/1804.07396v1 - - + + attachment Full Text - https://dacemirror.sci-hub.se/proceedings-article/dfcfc3f5502682650ac71b68af8f9b19/ward2009.pdf#view=FitH + https://arxiv.org/pdf/1804.07396v1.pdf - 2020-07-20 18:29:50 + 2020-07-20 18:28:01 1 application/pdf - - bookSection + + journalArticle - - Lecture Notes in Computer Science - + arxiv:1408.6315 + + + + + + Mehta + Rahul + + + + + + 2048 is (PSPACE) Hard, but Sometimes Easy + 2014 + + + http://arxiv.org/abs/1408.6315v1 + + + + + attachment + Full Text + + + https://arxiv.org/pdf/1408.6315v1.pdf + + + 2020-07-20 18:20:36 + 1 + application/pdf + + + journalArticle + + arxiv:1804.07393 + + + + + + Das + Madhuparna + + + + + Paul + Goutam + + + + + + Analysis of the Game "2048" and its Generalization in Higher Dimensions + 2018 + + + http://arxiv.org/abs/1804.07393v2 + + + + + attachment + Full Text + + + https://arxiv.org/pdf/1804.07393v2.pdf + + + 2020-07-20 18:21:31 + 1 + application/pdf + + + journalArticle + + arxiv:1603.00928 + + + + + + Almanza + Matteo + + + + + Leucci + Stefano + + + + + Panconesi + Alessandro + + + + + + Trainyard is NP-Hard + 2016 + + + http://arxiv.org/abs/1603.00928v1 + + + + + attachment + Full Text + + + https://arxiv.org/pdf/1603.00928v1.pdf + + + 2020-07-20 18:36:08 + 1 + application/pdf + + + journalArticle + + arxiv:1003.2851 - - - Springer Berlin Heidelberg - - @@ -1151,6 +6868,12 @@ Martin L. + + + Harvey + Nicholas J. 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2007 IEEE Symposium on Computational Intelligence and Games - DOI 10.1109/cig.2007.368089 + Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence + DOI 10.1145/3396474.3396492 - IEEE + ACM - Glenn - James R. + Grichshenko + Alexandr + + + + + Araújo + Luiz Jonatã Pires de + + + + + Gimaeva + Susanna + + + + + Brown + Joseph Alexander - - Computer Strategies for Solitaire Yahtzee - 2007 + + Using Tabu Search Algorithm for Map Generation in the Terra Mystica Tabletop Game + March 2020 - https://doi.org/10.1109%2Fcig.2007.368089 + https://doi.org/10.1145%2F3396474.3396492 - + attachment Submitted Version - http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=707B7E09A9652A1E4F2AB4BF608C410A?doi=10.1.1.111.1724&rep=rep1&type=pdf + https://arxiv.org/pdf/2006.02716 - 2020-07-20 18:09:04 + 2020-07-20 18:36:31 1 application/pdf - + journalArticle - - 18 - Expert Systems - DOI 10.1111/1468-0394.00160 - 2 - + arxiv:2003.05119 - Maynard - Ken - - - - - Moss - Patrick - - - - - Whitehead - Marcus - - - - - Narayanan - S. - - - - - Garay - Matt - - - - - Brannon - Nathan - - - - - Kantamneni - Raj Gopal - - - - - Kustra - Todd + Biderman + Stella - - Modeling expert problem solving in a game of chance: a Yahtzeec case study - May 2001 + + Magic: the Gathering is as Hard as Arithmetic + 2020 - https://doi.org/10.1111%2F1468-0394.00160 + http://arxiv.org/abs/2003.05119v1 - Publisher: Wiley - 88–98 - + attachment Full Text - https://cyber.sci-hub.se/MTAuMTExMS8xNDY4LTAzOTQuMDAxNjA=/maynard2001.pdf#view=FitH + https://arxiv.org/pdf/2003.05119v1.pdf - 2020-07-20 18:29:00 + 2020-07-20 18:27:42 1 application/pdf - - bookSection + + journalArticle - Computers and Games + arxiv:2004.13710 - - - Springer International Publishing - - - Oka - Kazuto + Canaan + Rodrigo - Matsuzaki - Kiminori + Gao + Xianbo + + + + + Togelius + Julian + + + + + Nealen + Andy + + + + + Menzel + Stefan - - Systematic Selection of N-Tuple Networks for 2048 + + Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi + 2020 + + + http://arxiv.org/abs/2004.13710v2 + + + + + attachment + Full Text + + + https://arxiv.org/pdf/2004.13710v2.pdf + + + 2020-07-20 18:25:19 + 1 + application/pdf + + + journalArticle + + arxiv:1603.01911 + + + + + + Baffier + Jean-Francois + + + + + Chiu + Man-Kwun + + + + + Diez + Yago + + + + + Korman + Matias + + + + + Mitsou + Valia + + + + + Renssen + André van + + + + + Roeloffzen + Marcel + + + + + Uno + Yushi + + + + + + Hanabi is NP-hard, Even for Cheaters who Look at Their Cards 2016 - 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- - attachment - Full Text - - - https://arxiv.org/pdf/1912.02318v1.pdf - - - 2020-07-20 18:26:28 - 1 - application/pdf - - - journalArticle - - arxiv:1603.01911 - - - - - - Baffier - Jean-Francois - - - - - Chiu - Man-Kwun - - - - - Diez - Yago - - - - - Korman - Matias - - - - - Mitsou - Valia - - - - - Renssen - André van - - - - - Roeloffzen - Marcel - - - - - Uno - Yushi - - - - - - Hanabi is NP-hard, Even for Cheaters who Look at Their Cards - 2016 - - - http://arxiv.org/abs/1603.01911v3 - - - - - attachment - Full Text - - - https://arxiv.org/pdf/1603.01911v3.pdf - - - 2020-07-20 18:25:31 - 1 - application/pdf - - - journalArticle - - arxiv:2004.13710 - - - - - - Canaan - Rodrigo - - - - - Gao - Xianbo - - - - - Togelius - Julian - - - - - Nealen - Andy - - - - - Menzel - Stefan - - - - - - Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi - 2020 - - - http://arxiv.org/abs/2004.13710v2 - - - - - attachment - Full Text - - - https://arxiv.org/pdf/2004.13710v2.pdf - - - 2020-07-20 18:25:19 - 1 - application/pdf - - - journalArticle - - arxiv:2004.13291 - - - - - - Canaan - Rodrigo - - - - - Gao - Xianbo - - - - - Chung - Youjin - - - - - Togelius - Julian - - - - - Nealen - Andy - - - - - Menzel - Stefan - - - - - - Evaluating the Rainbow DQN Agent in Hanabi with Unseen Partners - 2020 - - - http://arxiv.org/abs/2004.13291v1 - - - - - attachment - Full Text - - - https://arxiv.org/pdf/2004.13291v1.pdf - - - 2020-07-20 18:22:45 - 1 - application/pdf - - - journalArticle - - arxiv:2003.05119 - - - - - - Biderman - Stella - - - - - - Magic: the Gathering is as Hard as Arithmetic - 2020 - - - http://arxiv.org/abs/2003.05119v1 - - - - - attachment - Full Text - - - https://arxiv.org/pdf/2003.05119v1.pdf - - - 2020-07-20 18:27:42 - 1 - application/pdf - - - journalArticle - - arxiv:1904.09828 - - - - - - Churchill - Alex - - - - - Biderman - Stella - - - - - Herrick - Austin - - - - - - Magic: The Gathering is Turing Complete - 2019 - - - http://arxiv.org/abs/1904.09828v2 - - - - - attachment - Full Text - - - https://arxiv.org/pdf/1904.09828v2.pdf - - - 2020-07-20 18:27:51 - 1 - application/pdf - - - journalArticle - - arxiv:1810.03744 - - - - - - Zilio - Felipe - - - - - Prates - Marcelo - - - - - - Neural Networks Models for Analyzing Magic: the Gathering Cards - 2018 - - - http://arxiv.org/abs/1810.03744v1 - - - - - attachment - Full Text - - - https://arxiv.org/pdf/1810.03744v1.pdf - - - 2020-07-20 18:30:42 - 1 - application/pdf - - + conferencePaper - Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence - DOI 10.1145/3396474.3396492 + 2014 IEEE Conference on Computational Intelligence and Games + DOI 10.1109/cig.2014.6932920 - ACM + IEEE - Grichshenko - Alexandr + Rodgers + Philip - Araújo - Luiz Jonatã Pires de - - - - - Gimaeva - Susanna - - - - - Brown - Joseph Alexander + Levine + John - - Using Tabu Search Algorithm for Map Generation in the Terra Mystica Tabletop Game - March 2020 + + An investigation into 2048 AI strategies + August 2014 - https://doi.org/10.1145%2F3396474.3396492 + https://doi.org/10.1109%2Fcig.2014.6932920 - + + attachment + Full Text + + + https://zero.sci-hub.se/3377/2e196ce6e3cb06a636bf1ffdee8f5b6f/rodgers2014.pdf#view=FitH + + + 2020-07-20 18:21:23 + 1 + application/pdf + + + conferencePaper + + + 2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) + DOI 10.1109/taai.2016.7880154 + + + + IEEE + + + + + + Matsuzaki + Kiminori + + + + + + Systematic selection of N-tuple networks with consideration of interinfluence for game 2048 + November 2016 + + + https://doi.org/10.1109%2Ftaai.2016.7880154 + + + + + attachment + Full Text + + + https://twin.sci-hub.se/6299/d9bbecbbec212dab7fe6e6a67213b1cb/matsuzaki2016.pdf#view=FitH + + + 2020-07-20 18:32:39 + 1 + application/pdf + + + bookSection + + Computers and Games + + + + Springer International Publishing + + + + + + + Oka + Kazuto + + + + + Matsuzaki + Kiminori + + + + + + Systematic Selection of N-Tuple Networks for 2048 + 2016 + + + https://doi.org/10.1007%2F978-3-319-50935-8_8 + + + DOI: 10.1007/978-3-319-50935-8_8 + 81–92 + + + attachment + Full Text + + + https://sci-hub.se/downloads/2020-05-25/5f/oka2016.pdf#view=FitH + + + 2020-07-20 18:32:30 + 1 + application/pdf + + + conferencePaper + + + 2007 IEEE Symposium on Computational Intelligence and Games + DOI 10.1109/cig.2007.368089 + + + + IEEE + + + + + + Glenn + James R. + + + + + + Computer Strategies for Solitaire Yahtzee + 2007 + + + https://doi.org/10.1109%2Fcig.2007.368089 + + + + attachment Submitted Version - https://arxiv.org/pdf/2006.02716 + http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=707B7E09A9652A1E4F2AB4BF608C410A?doi=10.1.1.111.1724&rep=rep1&type=pdf - 2020-07-20 18:36:31 + 2020-07-20 18:09:04 1 application/pdf - + journalArticle - arxiv:1009.1031 + arXiv:2112.03178 [cs] - Migdał - Piotr + Schmid + Martin + + + + + Moravcik + Matej + + + + + Burch + Neil + + + + + Kadlec + Rudolf + + + + + Davidson + Josh + + + + + Waugh + Kevin + + + + + Bard + Nolan + + + + + Timbers + Finbarr + + + + + Lanctot + Marc + + + + + Holland + Zach + + + + + Davoodi + Elnaz + + + + + Christianson + Alden + + + + + Bowling + Michael - - A mathematical model of the Mafia game - 2010 + + + + + Computer Science - Artificial Intelligence + + + + + Computer Science - Machine Learning + + + + + Computer Science - Computer Science and Game Theory + + + Player of Games + Games have a long history of serving as a benchmark for progress in artificial intelligence. Recently, approaches using search and learning have shown strong performance across a set of perfect information games, and approaches using game-theoretic reasoning and learning have shown strong performance for specific imperfect information poker variants. We introduce Player of Games, a general-purpose algorithm that unifies previous approaches, combining guided search, self-play learning, and game-theoretic reasoning. Player of Games is the first algorithm to achieve strong empirical performance in large perfect and imperfect information games -- an important step towards truly general algorithms for arbitrary environments. We prove that Player of Games is sound, converging to perfect play as available computation time and approximation capacity increases. Player of Games reaches strong performance in chess and Go, beats the strongest openly available agent in heads-up no-limit Texas hold'em poker (Slumbot), and defeats the state-of-the-art agent in Scotland Yard, an imperfect information game that illustrates the value of guided search, learning, and game-theoretic reasoning. + 2021-12-06 + arXiv.org - http://arxiv.org/abs/1009.1031v3 + http://arxiv.org/abs/2112.03178 + 2021-12-08 07:04:53 + arXiv: 2112.03178 - + attachment - Full Text + arXiv Fulltext PDF - https://arxiv.org/pdf/1009.1031v3.pdf + https://arxiv.org/pdf/2112.03178.pdf - 2020-07-20 18:20:44 + 2021-12-08 07:05:54 1 application/pdf - + + attachment + arXiv.org Snapshot + + + https://arxiv.org/abs/2112.03178 + + + 2021-12-08 07:06:43 + 1 + text/html + + journalArticle - - arxiv:1003.2851 - + - Demaine - Erik D. - - - - - Demaine - Martin L. - - - - - Harvey - Nicholas J. A. - - - - - Uehara - Ryuhei - - - - - Uno - Takeaki - - - - - Uno - Yushi - - - - - - The complexity of UNO - 2010 - - - http://arxiv.org/abs/1003.2851v3 - - - - - attachment - Full Text - - - https://arxiv.org/pdf/1003.2851v3.pdf - - - 2020-07-20 18:34:43 - 1 - application/pdf - - - journalArticle - - arxiv:1603.00928 - - - - - - Almanza - Matteo - - - - - Leucci - Stefano - - - - - Panconesi - Alessandro - - - - - - Trainyard is NP-Hard - 2016 - - - http://arxiv.org/abs/1603.00928v1 - - - - - attachment - Full Text - - - https://arxiv.org/pdf/1603.00928v1.pdf - - - 2020-07-20 18:36:08 - 1 - application/pdf - - - journalArticle - - arxiv:1505.04274 - - - - - - Langerman - Stefan - - - - - Uno - Yushi - - - - - - Threes!, Fives, 1024!, and 2048 are Hard - 2015 - - - http://arxiv.org/abs/1505.04274v1 - - - - - attachment - Full Text - - - https://arxiv.org/pdf/1505.04274v1.pdf - - - 2020-07-20 18:35:46 - 1 - application/pdf - - - journalArticle - - arxiv:1804.07396 - - - - - - Eppstein + Silver David - - - - Making Change in 2048 - 2018 - - - http://arxiv.org/abs/1804.07396v1 - - - - - attachment - Full Text - - - https://arxiv.org/pdf/1804.07396v1.pdf - - - 2020-07-20 18:28:01 - 1 - application/pdf - - - journalArticle - - arxiv:1804.07393 - - - - Das - Madhuparna + Schrittwieser + Julian - Paul - Goutam + Simonyan + Karen + + + + + Antonoglou + Ioannis + + + + + Huang + Aja + + + + + Guez + Arthur + + + + + Hubert + Thomas + + + + + Baker + Lucas + + + + + Lai + Matthew + + + + + Bolton + Adrian + + + + + Chen + Yutian + + + + + Lillicrap + Timothy + + + + + Hui + Fan + + + + + Sifre + Laurent + + + + + van den Driessche + George + + + + + Graepel + Thore + + + + + Hassabis + Demis - - Analysis of the Game "2048" and its Generalization in Higher Dimensions - 2018 + Mastering the game of Go without human knowledge + 10/2017 + en + DOI.org (Crossref) - http://arxiv.org/abs/1804.07393v2 + http://www.nature.com/articles/nature24270 + 2021-12-08 07:05:30 + 354-359 - - attachment - Full Text - - - https://arxiv.org/pdf/1804.07393v2.pdf - - - 2020-07-20 18:21:31 - 1 - application/pdf - - - journalArticle + + 550 + Nature + DOI 10.1038/nature24270 + 7676 + Nature + ISSN 0028-0836, 1476-4687 + + + conferencePaper - arxiv:1606.07374 + + ISBN 978-1-72811-229-9 + 2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) + DOI 10.1109/TAAI.2018.00034 + + + + + + Taichung + + + IEEE + + - - - Yeh - Kun-Hao - - - - - Wu - I.-Chen - - Hsueh @@ -2782,5233 +8198,261 @@ - Chang - Chia-Chuan + Wu + I-Chen - Liang - Chao-Chin + Chen + Jr-Chang - - - Chiang - Han - - - - - - Multi-Stage Temporal Difference Learning for 2048-like Games - 2016 - - - http://arxiv.org/abs/1606.07374v2 - - - - - attachment - Full Text - - - https://arxiv.org/pdf/1606.07374v2.pdf - - - 2020-07-20 18:30:19 - 1 - application/pdf - - - journalArticle - - arxiv:1408.6315 - - - - - - Mehta - Rahul - - - - - - 2048 is (PSPACE) Hard, but Sometimes Easy - 2014 - - - http://arxiv.org/abs/1408.6315v1 - - - - - attachment - Full Text - - - https://arxiv.org/pdf/1408.6315v1.pdf - - - 2020-07-20 18:20:36 - 1 - application/pdf - - - computerProgram - Settlers of Catan bot trained using reinforcement learning - - - https://jonzia.github.io/Catan/ - - - MATLAB - - - conferencePaper - - - 34 - Proceedings of the Annual Meeting of the Cognitive Science Society - - - - - - - Guhe - Markus - - - - - Lascarides - Alex - - - - - - Trading in a multiplayer board game: Towards an analysis of non-cooperative dialogue - 2012 - - - https://escholarship.org/uc/item/9zt506xx - - - Issue: 34 - - - attachment - Guhe and Lascarides - 2012 - Trading in a multiplayer board game Towards an an.pdf - application/pdf - - - journalArticle - - - - - POMCP with Human Preferencesin Settlers of Catan - - - https://www.aaai.org/ocs/index.php/AIIDE/AIIDE18/paper/viewFile/18091/17217 - - - - - attachment - POMCP with Human Preferencesin Settlers of Catan.pdf - application/pdf - - - blogPost - - - - The impact of loaded dice in Catan - - - https://izbicki.me/blog/how-to-cheat-at-settlers-of-catan-by-loading-the-dice-and-prove-it-with-p-values.html - - - - - journalArticle - - - - - Monte Carlo Tree Search in a Modern Board Game Framework - - - https://project.dke.maastrichtuniversity.nl/games/files/bsc/Roelofs_Bsc-paper.pdf - - - - - attachment - Full Text - - - https://project.dke.maastrichtuniversity.nl/games/files/bsc/Roelofs_Bsc-paper.pdf - - - 2020-07-20 18:47:19 - 1 - application/pdf - - - conferencePaper - - - - - - - - Pfeiffer - Michael - - - - - - Reinforcement Learning of Strategies for Settlers of Catan - 2004 - - - https://www.researchgate.net/publication/228728063_Reinforcement_learning_of_strategies_for_Settlers_of_Catan - - - - - attachment - Pfeiffer - 2004 - Reinforcement Learning of Strategies for Settlers .pdf - application/pdf - - - presentation - - - - - Michael Wolf - - - - - - An Intelligent Artificial Player for the Game of Risk - 20/04/2005 - - - http://www.ke.tu-darmstadt.de/lehre/archiv/ss04/oberseminar/folien/Wolf_Michael-Slides.pdf - - - - - attachment - An Intelligent Artificial Player for the Game of R.pdf - application/pdf - - - journalArticle - - - - - RISKy Business: An In-Depth Look at the Game RISK - - - https://scholar.rose-hulman.edu/rhumj/vol3/iss2/3 - - - - - attachment - RISKy Business An In-Depth Look at the Game RISK.pdf - application/pdf - - - journalArticle - - - - - RISK Board Game ‐ Battle Outcome Analysis - - - http://www.c4i.gr/xgeorgio/docs/RISK-board-game%20_rev-3.pdf - - - - - attachment - Full Text - - - https://www.c4i.gr/xgeorgio/docs/RISK-board-game%20_rev-3.pdf - - - 2020-07-20 18:54:23 - 1 - application/pdf - - - thesis - - - Blekinge Institute of Technology, School of Engineering, Department of Systems and Software Engineering. - - - - - - - Olsson - Fredrik - - - - - - - A multi-agent system for playing the board game risk - Risk is a game in which traditional Artificial-Intelligence methods such as for example iterative deepening and Alpha-Beta pruning can not successfully be applied due to the size of the search space. Distributed problem solving in the form of a multi-agent system might be the solution. This needs to be tested before it is possible to tell if a multi-agent system will be successful at playing Risk or not. In this thesis the development of a multi-agent system that plays Risk is explained. The system places an agent in every country on the board and uses a central agent for organizing communication. An auction mechanism is used for negotiation. The experiments show that a multi-agent solution indeed is a prosperous approach when developing a computer based player for the board game Risk. - 2005 - - - http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3781 - - - 51 - Independent thesis Advanced level (degree of Master (One Year)) - - - attachment - Full Text - - - http://bth.diva-portal.org/smash/get/diva2:831093/FULLTEXT01 - - - 2021-07-24 08:26:48 - 1 - application/pdf - - - attachment - Full Text - - - https://www.diva-portal.org/smash/get/diva2:831093/FULLTEXT01.pdf - - - 2021-07-24 08:28:25 - 3 - - - blogPost - - - - State Representation and Polyomino Placement for the Game Patchwork - - - https://zayenz.se/blog/post/patchwork-modref2019-paper/ - - - - - journalArticle - - arXiv:2001.04233 [cs] - - - - - - Lagerkvist - Mikael Zayenz - - - - - - - - - Computer Science - Artificial Intelligence - - - State Representation and Polyomino Placement for the Game Patchwork - Modern board games are a rich source of entertainment for many people, but also contain interesting and challenging structures for game playing research and implementing game playing agents. This paper studies the game Patchwork, a two player strategy game using polyomino tile drafting and placement. The core polyomino placement mechanic is implemented in a constraint model using regular constraints, extending and improving the model in (Lagerkvist, Pesant, 2008) with: explicit rotation handling; optional placements; and new constraints for resource usage. Crucial for implementing good game playing agents is to have great heuristics for guiding the search when faced with large branching factors. This paper divides placing tiles into two parts: a policy used for placing parts and an evaluation used to select among different placements. Policies are designed based on classical packing literature as well as common standard constraint programming heuristics. For evaluation, global propagation guided regret is introduced, choosing placements based on not ruling out later placements. Extensive evaluations are performed, showing the importance of using a good evaluation and that the proposed global propagation guided regret is a very effective guide. - 2020-01-13 - arXiv.org - - - http://arxiv.org/abs/2001.04233 - - - 2020-07-21 10:55:58 - arXiv: 2001.04233 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/2001.04233.pdf - - - 2020-07-21 10:56:09 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/2001.04233 - - - 2020-07-21 10:56:13 - 1 - text/html - - - presentation - - State Representation and Polyomino Placement for the Game Patchwork - - - https://zayenz.se/papers/Lagerkvist_ModRef_2019_Presentation.pdf - - - - - attachment - Full Text - - - https://zayenz.se/papers/Lagerkvist_ModRef_2019_Presentation.pdf - - - 2020-07-21 10:56:59 - 1 - application/pdf - - - journalArticle - - arXiv:2001.04238 [cs] - - - - - - Lagerkvist - Mikael Zayenz - - - - - - - - - Computer Science - Artificial Intelligence - - - Nmbr9 as a Constraint Programming Challenge - Modern board games are a rich source of interesting and new challenges for combinatorial problems. The game Nmbr9 is a solitaire style puzzle game using polyominoes. The rules of the game are simple to explain, but modelling the game effectively using constraint programming is hard. This abstract presents the game, contributes new generalized variants of the game suitable for benchmarking and testing, and describes a model for the presented variants. The question of the top possible score in the standard game is an open challenge. - 2020-01-13 - arXiv.org - - - http://arxiv.org/abs/2001.04238 - - - 2020-07-21 10:57:58 - arXiv: 2001.04238 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/2001.04238.pdf - - - 2020-07-21 10:58:01 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/2001.04238 - - - 2020-07-21 10:58:02 - 1 - text/html - - - blogPost - - - - Nmbr9 as a Constraint Programming Challenge - - - https://zayenz.se/blog/post/nmbr9-cp2019-abstract/ - - - - - conferencePaper - - - DOI 10.1109/CIG.2019.8848097 - - - - - - - Goodman - James - - - - - - Re-determinizing MCTS in Hanabi - 08 2019 - 1-8 - - - attachment - Goodman - 2019 - Re-determinizing MCTS in Hanabi.pdf - application/pdf - - - conferencePaper - - - ISBN 978-1-5386-4359-4 - 2018 IEEE Conference on Computational Intelligence and Games (CIG) - DOI 10.1109/CIG.2018.8490449 - - - - - - - Maastricht - - - IEEE - - - - - - - Canaan - Rodrigo - - - - - Shen - Haotian - - - - - Torrado - Ruben - - - - - Togelius - Julian - - - - - Nealen - Andy - - - - - Menzel - Stefan - - - - - - Evolving Agents for the Hanabi 2018 CIG Competition - 8/2018 - DOI.org (Crossref) - - - https://ieeexplore.ieee.org/document/8490449/ - - - 2020-07-21 11:01:52 - 1-8 - - - 2018 IEEE Conference on Computational Intelligence and Games (CIG) - - - - - attachment - Submitted Version - - - https://arxiv.org/pdf/1809.09764 - - - 2020-07-21 11:01:56 - 1 - application/pdf - - - bookSection - - - 765 - ISBN 978-3-319-67467-4 978-3-319-67468-1 - BNAIC 2016: Artificial Intelligence - - - - - - - Cham - - - Springer International Publishing - - - - - - - Bosse - Tibor - - - - - Bredeweg - Bert - - - - - - - - - van den Bergh - Mark J. H. - - - - - Hommelberg - Anne - - - - - Kosters - Walter A. - - - - - Spieksma - Flora M. - - - - - - Aspects of the Cooperative Card Game Hanabi - 2017 - DOI.org (Crossref) - - - http://link.springer.com/10.1007/978-3-319-67468-1_7 - - - 2020-07-21 11:02:26 - Series Title: Communications in Computer and Information Science -DOI: 10.1007/978-3-319-67468-1_7 - 93-105 - - - attachment - Full Text - - - https://twin.sci-hub.se/6548/49fca9bfed767f739defcd030c004bdb/vandenbergh2017.pdf#view=FitH - - - 2020-07-21 11:02:31 - 1 - application/pdf - - - bookSection - - - 10664 - ISBN 978-3-319-71648-0 978-3-319-71649-7 - Advances in Computer Games - - - - - - - Cham - - - Springer International Publishing - - - - - - - Winands - Mark H.M. - - - - - van den Herik - H. Jaap - - - - - Kosters - Walter A. - - - - - - - - - Bouzy - Bruno - - - - - Playing Hanabi Near-Optimally - 2017 - DOI.org (Crossref) - - - http://link.springer.com/10.1007/978-3-319-71649-7_5 - - - 2020-07-21 11:02:53 - Series Title: Lecture Notes in Computer Science -DOI: 10.1007/978-3-319-71649-7_5 - 51-62 - - - conferencePaper - - - ISBN 978-1-5386-3233-8 - 2017 IEEE Conference on Computational Intelligence and Games (CIG) - DOI 10.1109/CIG.2017.8080417 - - - - - - - New York, NY, USA - - - IEEE - - - - - - - Eger - Markus - - - - - Martens - Chris - - - - - Cordoba - Marcela Alfaro - - - - - - An intentional AI for hanabi - 8/2017 - DOI.org (Crossref) - - - http://ieeexplore.ieee.org/document/8080417/ - - - 2020-07-21 11:03:36 - 68-75 - - - 2017 IEEE Conference on Computational Intelligence and Games (CIG) - - - - - attachment - Full Text - - - https://zero.sci-hub.se/6752/bcf6e994ee7503ab821bd67848727b05/eger2017.pdf#view=FitH - - - 2020-07-21 11:03:40 - 1 - application/pdf - - - conferencePaper - - - - - - - - Osawa - Hirotaka - - - - - - Solving Hanabi: Estimating Hands by Opponent's Actions in Cooperative Game with Incomplete Information - A unique behavior of humans is modifying one’s unobservable behavior based on the reaction of others for cooperation. We used a card game called Hanabi as an evaluation task of imitating human reflective intelligence with artificial intelligence. Hanabi is a cooperative card game with incomplete information. A player cooperates with an opponent in building several card sets constructed with the same color and ordered numbers. However, like a blind man's bluff, each player sees the cards of all other players except his/her own. Also, communication between players is restricted to information about the same numbers and colors, and the player is required to read his/his opponent's intention with the opponent's hand, estimate his/her cards with incomplete information, and play one of them for building a set. We compared human play with several simulated strategies. The results indicate that the strategy with feedbacks from simulated opponent's viewpoints achieves more score than other strategies. - 2015 - - - https://aaai.org/ocs/index.php/WS/AAAIW15/paper/view/10167 - - - - AAAI Workshops - - - - attachment - Osawa - 2015 - Solving Hanabi Estimating Hands by Opponent's Act.pdf - application/pdf - - - journalArticle - - Cape Cod - - - - - - Eger - Markus - - - - - Martens - Chris - - - - - - A Browser-based Interface for the Exploration and Evaluation of Hanabi AIs - 2017 - en - Zotero - - - http://fdg2017.org/papers/FDG2017_demo_Hanabi.pdf - - - 4 - - - attachment - Eger and Martens - 2017 - A Browser-based Interface for the Exploration and .pdf - application/pdf - - - journalArticle - - - - - - - - Gottwald - Eva Tallula - - - - - Eger - Markus - - - - - Martens - Chris - - - - - - I see what you see: Integrating eye tracking into Hanabi playing agents - Humans’ eye movements convey a lot of information about their intentions, often unconsciously. Intelligent agents that cooperate with humans in various domains can benefit from interpreting this information. This paper contains a preliminary look at how eye tracking could be useful for agents that play the cooperative card game Hanabi with human players. We outline several situations in which an AI agent can utilize gaze information, and present an outlook on how we plan to integrate this with reimplementations of contemporary Hanabi agents. - en - Zotero - 4 - - - attachment - Gottwald et al. - I see what you see Integrating eye tracking into .pdf - application/pdf - - - computerProgram - State of the art Hanabi bots + simulation framework in rust - - - https://github.com/WuTheFWasThat/hanabi.rs - - - - - computerProgram - A strategy simulator for the well-known cooperative card game Hanabi - - - https://github.com/rjtobin/HanSim - - - - - computerProgram - A framework for writing bots that play Hanabi - - - https://github.com/Quuxplusone/Hanabi - - - - - journalArticle - - - - - Ludic Language Pedagogy - - - Ludic Language Pedagogy - - - - - - - deHaan - Jonathan - - - - - - Jidoukan Jenga: Teaching English through remixing games and game rules - Let students play simple games in their L1. It’s ok! - - Then: - - You, the teacher, can help them critique the game in their L2. - You, the teacher, can help them change the game in their L2. - You, the teacher, can help them develop themselves. - - #dropthestick #dropthecarrot #bringmeaning - 2020-04-15 - Teaching English through remixing games and game rules - - - https://www.llpjournal.org/2020/04/13/jidokan-jenga.html - - - 📍 What is this? This is a recollection of a short lesson with some children. I used Jenga and a dictionary. - 📍 Why did you make it? I want to show language teachers that simple games, and playing simple games in students’ first language can be a great foundation for helping students learn new vocabulary, think critically, and exercise creativity. - 📍 Why is it radical? I taught using a simple board game (at a time when video games are over-focused on in research). I show what the learning looks like (I include a photo). The teaching and learning didn’t occur in a laboratory setting, but in the wild (in a community center). I focused on the learning around games. - 📍 Who is it for? Language teachers can easily implement this lesson using Jenga or any other game. Language researchers can expand on the translating and remixing potential around games. - - - attachment - deHaan - 2020 - Jidoukan Jenga Teaching English through remixing .pdf - application/pdf - - - journalArticle - - - - - - Heron - Michael James - - - - - Belford - Pauline Helen - - - - - Reid - Hayley - - - - - Crabb - Michael - - - - - - Meeple Centred Design: A Heuristic Toolkit for Evaluating the Accessibility of Tabletop Games - 6/2018 - en - Meeple Centred Design - DOI.org (Crossref) - - - http://link.springer.com/10.1007/s40869-018-0057-8 - - - 2020-07-28 09:08:52 - 97-114 - - - 7 - The Computer Games Journal - DOI 10.1007/s40869-018-0057-8 - 2 - Comput Game J - ISSN 2052-773X - - - attachment - Full Text - - - https://link.springer.com/content/pdf/10.1007/s40869-018-0057-8.pdf - - - 2020-07-28 09:08:55 - 1 - application/pdf - - - journalArticle - - - 7 - The Computer Games Journal - DOI 10.1007/s40869-018-0056-9 - 2 - Comput Game J - ISSN 2052-773X - - - - - - - Heron - Michael James - - - - - Belford - Pauline Helen - - - - - Reid - Hayley - - - - - Crabb - Michael - - - - - - Eighteen Months of Meeple Like Us: An Exploration into the State of Board Game Accessibility - 6/2018 - en - Eighteen Months of Meeple Like Us - DOI.org (Crossref) - - - http://link.springer.com/10.1007/s40869-018-0056-9 - - - 2020-07-28 09:09:05 - 75-95 - - - attachment - Full Text - - - https://link.springer.com/content/pdf/10.1007/s40869-018-0056-9.pdf - - - 2020-07-28 09:09:08 - 1 - application/pdf - - - thesis - - - Utrecht University - - - - - - - Andel - Daniël - - - - - - On the complexity of Hive - It is shown that for an arbitrary position of a Hive game where both players have the same set of N pieces it is PSPACE-hard to determine whether one of the players has a winning strategy. The proof is done by reducing the known PSPACE-complete set of true quantified boolean formulas to a game concerning these formulas, then to the game generalised geography, then to a version of that game with the restriction of having only nodes with maximum degree 3, and finally to generalised Hive. This thesis includes a short introduction to the subject of computational complexity. - May 2020 - en-US - On the complexity of Hive - - - https://dspace.library.uu.nl/handle/1874/396955 - - - 33 - Bachelor thesis - - - attachment - Andel - 2020 - On the complexity of Hive.pdf - application/pdf - - - journalArticle - - arXiv:2010.00048 [cs] - - - - - - Kunda - Maithilee - - - - - Rabkina - Irina - - - - - - - - - Computer Science - Artificial Intelligence - - - Creative Captioning: An AI Grand Challenge Based on the Dixit Board Game - We propose a new class of "grand challenge" AI problems that we call creative captioning---generating clever, interesting, or abstract captions for images, as well as understanding such captions. Creative captioning draws on core AI research areas of vision, natural language processing, narrative reasoning, and social reasoning, and across all these areas, it requires sophisticated uses of common sense and cultural knowledge. In this paper, we analyze several specific research problems that fall under creative captioning, using the popular board game Dixit as both inspiration and proposed testing ground. We expect that Dixit could serve as an engaging and motivating benchmark for creative captioning across numerous AI research communities for the coming 1-2 decades. - 2020-09-30 - Creative Captioning - arXiv.org - - - http://arxiv.org/abs/2010.00048 - - - 2020-10-12 04:03:28 - arXiv: 2010.00048 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/2010.00048.pdf - - - 2020-10-12 04:03:46 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/2010.00048 - - - 2020-10-12 04:03:53 - 1 - text/html - - - computerProgram - Shobu AI Playground - - - https://github.com/JayWalker512/Shobu - - - - - webpage - - - - Shobu randomly played games dataset - - - https://www.kaggle.com/bsfoltz/shobu-randomly-played-games-104k - - - - - conferencePaper - - - ISBN 978-1-4503-5319-9 - Proceedings of the International Conference on the Foundations of Digital Games - FDG '17 - DOI 10.1145/3102071.3102105 - - - - - - - Hyannis, Massachusetts - - - ACM Press - - - - - - - de Mesentier Silva - Fernando - - - - - Lee - Scott - - - - - Togelius - Julian - - - - - Nealen - Andy - - - - - - - AI-based playtesting of contemporary board games - 2017 - en - DOI.org (Crossref) - - - http://dl.acm.org/citation.cfm?doid=3102071.3102105 - - - 2020-10-12 04:09:30 - 1-10 - - - the International Conference - - - - - attachment - Full Text - - - https://twin.sci-hub.se/6553/d80b9cdf7f993e1137d0b129dec94e6d/demesentiersilva2017.pdf#view=FitH - - - 2020-10-12 04:09:38 - 1 - application/pdf - - - attachment - PDF - - - http://game.engineering.nyu.edu/wp-content/uploads/2017/06/ticket-ride-fdg2017-camera-ready.pdf - - - 2020-10-12 04:13:00 - 3 - - - computerProgram - - - - - Copley - Rowan - - - - - Materials for Ticket to Ride Seattle and a framework for making more game boards - - - https://github.com/dovinmu/ttr_generator - - - - - report - - - - - Nguyen - Cuong - - - - - Dinjian - Daniel - - - - - - The Difficulty of Learning Ticket to Ride - Ticket to Ride is a very popular, award-winning board-game where you try toscore the most points while building a railway spanning cities in America. For acomputer to learn to play this game is very difficult due to the vast state-actionspace. This project will explain why featurizing your state, and implementingcurriculum learning can help agents learn as state-action spaces grow too largefor traditional learning methods to be effective. - - - https://www.eecs.tufts.edu/~jsinapov/teaching/comp150_RL/reports/Nguyen_Dinjian_report.pdf - - - - - attachment - Full Text - - - https://www.eecs.tufts.edu/~jsinapov/teaching/comp150_RL/reports/Nguyen_Dinjian_report.pdf - - - 2021-07-24 08:19:13 - 1 - application/pdf - - - conferencePaper - - - ISBN 978-1-4503-6571-0 - Proceedings of the 13th International Conference on the Foundations of Digital Games - DOI 10.1145/3235765.3235813 - - - - - - - Malmö Sweden - - - ACM - - - - - - - de Mesentier Silva - Fernando - - - - - Lee - Scott - - - - - Togelius - Julian - - - - - Nealen - Andy - - - - - - Evolving maps and decks for ticket to ride - 2018-08-07 - en - DOI.org (Crossref) - - - https://dl.acm.org/doi/10.1145/3235765.3235813 - - - 2020-10-12 04:12:33 - 1-7 - - - FDG '18: Foundations of Digital Games 2018 - - - - - attachment - Full Text - - - https://twin.sci-hub.se/7128/24e28b0429626f565aafd93768332e73/demesentiersilva2018.pdf#view=FitH - - - 2020-10-12 04:12:36 - 1 - application/pdf - - - journalArticle - - - arXiv:2008.07079 [cs, stat] - - - - - - - Gendre - Quentin - - - - - Kaneko - Tomoyuki - - - - - - - - - Computer Science - Artificial Intelligence - - - - - Computer Science - Machine Learning - - - - - Statistics - Machine Learning - - - Playing Catan with Cross-dimensional Neural Network - Catan is a strategic board game having interesting properties, including multi-player, imperfect information, stochastic, complex state space structure (hexagonal board where each vertex, edge and face has its own features, cards for each player, etc), and a large action space (including negotiation). Therefore, it is challenging to build AI agents by Reinforcement Learning (RL for short), without domain knowledge nor heuristics. In this paper, we introduce cross-dimensional neural networks to handle a mixture of information sources and a wide variety of outputs, and empirically demonstrate that the network dramatically improves RL in Catan. We also show that, for the first time, a RL agent can outperform jsettler, the best heuristic agent available. - 2020-08-17 - arXiv.org - - - http://arxiv.org/abs/2008.07079 - - - 2020-10-12 04:19:57 - arXiv: 2008.07079 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/2008.07079.pdf - - - 2020-10-12 04:20:04 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/2008.07079 - - - 2020-10-12 04:20:10 - 1 - text/html - - - conferencePaper - - - ISBN 978-1-4503-8878-8 - 11th Hellenic Conference on Artificial Intelligence - DOI 10.1145/3411408.3411413 - - - - - - - Athens Greece - - - ACM - - - - - - - Theodoridis - Alexios - - - - - Chalkiadakis - Georgios - - - - - Monte Carlo Tree Search for the Game of Diplomacy - 2020-09-02 - en - DOI.org (Crossref) - - - https://dl.acm.org/doi/10.1145/3411408.3411413 - - - 2020-10-12 04:20:38 - 16-25 - - - SETN 2020: 11th Hellenic Conference on Artificial Intelligence - - - - - journalArticle - - - - - - Eger - Markus - - - - - Martens - Chris - - - - - Sauma Chacon - Pablo - - - - - Alfaro Cordoba - Marcela - - - - - Hidalgo Cespedes - Jeisson - - - - - - Operationalizing Intentionality to Play Hanabi with Human Players - 2020 - DOI.org (Crossref) - - - https://ieeexplore.ieee.org/document/9140404/ - - - 2020-11-26 08:48:44 - 1-1 - - - IEEE Transactions on Games - DOI 10.1109/TG.2020.3009359 - IEEE Trans. Games - ISSN 2475-1502, 2475-1510 - - - attachment - Full Text - - - https://sci-hub.se/downloads/2020-08-17/f1/eger2020.pdf?rand=5fbf6bef76c6b#view=FitH - - - 2020-11-26 08:48:52 - 1 - application/pdf - - - journalArticle - - - 16 - Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment - 1 - AIIDE - - - - - - - Canaan - Rodrigo - - - - - Gao - Xianbo - - - - - Chung - Youjin - - - - - Togelius - Julian - - - - - Nealen - Andy - - - - - Menzel - Stefan - - - - - - Behavioral Evaluation of Hanabi Rainbow DQN Agents and Rule-Based Agents - <p class="abstract">Hanabi is a multiplayer cooperative card game, where only your partners know your cards. All players succeed or fail together. This makes the game an excellent testbed for studying collaboration. Recently, it has been shown that deep neural networks can be trained through self-play to play the game very well. However, such agents generally do not play well with others. In this paper, we investigate the consequences of training Rainbow DQN agents with human-inspired rule-based agents. We analyze with which agents Rainbow agents learn to play well, and how well playing skill transfers to agents they were not trained with. We also analyze patterns of communication between agents to elucidate how collaboration happens. A key finding is that while most agents only learn to play well with partners seen during training, one particular agent leads the Rainbow algorithm towards a much more general policy. The metrics and hypotheses advanced in this paper can be used for further study of collaborative agents.</p> - October 1, 2020 - - - https://ojs.aaai.org/index.php/AIIDE/article/view/7404 - - - 2020-11-26 - Section: Full Oral Papers - 31-37 - - - attachment - View PDF - - - https://ojs.aaai.org/index.php/AIIDE/article/view/7404/7333 - - - 2020-11-26 08:52:38 - 3 - - - conferencePaper - - - 2020第82回全国大会講演論文集 - - - - - - - ひい - とう - - - - - 市来 - 正裕 - - - - - 中里 - 研一 - - - - - - Playing mini-Hanabi card game with Q-learning - February 2020 - - - http://id.nii.ac.jp/1001/00205046/ - - - Issue: 1 - 41–42 - - - attachment - View PDF - - - https://ipsj.ixsq.nii.ac.jp/ej/?action=repository_uri&item_id=205142&file_id=1&file_no=1 - - - 2020-11-26 08:54:47 - 3 - - - journalArticle - - arXiv:2005.07156 [cs] - - - - - - Reinhardt - Jack - - - - - - - - - Computer Science - Artificial Intelligence - - - - - Computer Science - Multiagent Systems - - - Competing in a Complex Hidden Role Game with Information Set Monte Carlo Tree Search - Advances in intelligent game playing agents have led to successes in perfect information games like Go and imperfect information games like Poker. The Information Set Monte Carlo Tree Search (ISMCTS) family of algorithms outperforms previous algorithms using Monte Carlo methods in imperfect information games. In this paper, Single Observer Information Set Monte Carlo Tree Search (SO-ISMCTS) is applied to Secret Hitler, a popular social deduction board game that combines traditional hidden role mechanics with the randomness of a card deck. This combination leads to a more complex information model than the hidden role and card deck mechanics alone. It is shown in 10108 simulated games that SO-ISMCTS plays as well as simpler rule based agents, and demonstrates the potential of ISMCTS algorithms in complicated information set domains. - 2020-05-14 - arXiv.org - - - http://arxiv.org/abs/2005.07156 - - - 2020-11-26 09:00:33 - arXiv: 2005.07156 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/2005.07156.pdf - - - 2020-11-26 09:01:03 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/2005.07156 - - - 2020-11-26 09:01:10 - 1 - text/html - - - journalArticle - - arXiv:2009.12974 [cs] - - - - - - Ameneyro - Fred Valdez - - - - - Galvan - Edgar - - - - - Morales - Anger Fernando Kuri - - - - - - - - - Computer Science - Artificial Intelligence - - - Playing Carcassonne with Monte Carlo Tree Search - Monte Carlo Tree Search (MCTS) is a relatively new sampling method with multiple variants in the literature. They can be applied to a wide variety of challenging domains including board games, video games, and energy-based problems to mention a few. In this work, we explore the use of the vanilla MCTS and the MCTS with Rapid Action Value Estimation (MCTS-RAVE) in the game of Carcassonne, a stochastic game with a deceptive scoring system where limited research has been conducted. We compare the strengths of the MCTS-based methods with the Star2.5 algorithm, previously reported to yield competitive results in the game of Carcassonne when a domain-specific heuristic is used to evaluate the game states. We analyse the particularities of the strategies adopted by the algorithms when they share a common reward system. The MCTS-based methods consistently outperformed the Star2.5 algorithm given their ability to find and follow long-term strategies, with the vanilla MCTS exhibiting a more robust game-play than the MCTS-RAVE. - 2020-10-04 - arXiv.org - - - http://arxiv.org/abs/2009.12974 - - - 2021-01-02 18:13:09 - arXiv: 2009.12974 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/2009.12974.pdf - - - 2021-01-02 18:13:12 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/2009.12974 - - - 2021-01-02 18:13:17 - 1 - text/html - - - journalArticle - - arXiv:2007.15895 [cs] - - - - - - Tanaka - Satoshi - - - - - Bonnet - François - - - - - Tixeuil - Sébastien - - - - - Tamura - Yasumasa - - - - - - - - - Computer Science - Computer Science and Game Theory - - - Quixo Is Solved - Quixo is a two-player game played on a 5$\times$5 grid where the players try to align five identical symbols. Specifics of the game require the usage of novel techniques. Using a combination of value iteration and backward induction, we propose the first complete analysis of the game. We describe memory-efficient data structures and algorithmic optimizations that make the game solvable within reasonable time and space constraints. Our main conclusion is that Quixo is a Draw game. The paper also contains the analysis of smaller boards and presents some interesting states extracted from our computations. - 2020-07-31 - arXiv.org - - - http://arxiv.org/abs/2007.15895 - - - 2021-01-02 18:17:10 - arXiv: 2007.15895 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/2007.15895.pdf - - - 2021-01-02 18:17:17 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/2007.15895 - - - 2021-01-02 18:17:21 - 1 - text/html - - - journalArticle - - arXiv:2006.02353 [cs] - - - - - - Bertholon - Guillaume - - - - - Géraud-Stewart - Rémi - - - - - Kugelmann - Axel - - - - - Lenoir - Théo - - - - - Naccache - David - - - - - - - - - Computer Science - Computer Science and Game Theory - - - At Most 43 Moves, At Least 29: Optimal Strategies and Bounds for Ultimate Tic-Tac-Toe - Ultimate Tic-Tac-Toe is a variant of the well known tic-tac-toe (noughts and crosses) board game. Two players compete to win three aligned "fields", each of them being a tic-tac-toe game. Each move determines which field the next player must play in. We show that there exist a winning strategy for the first player, and therefore that there exist an optimal winning strategy taking at most 43 moves; that the second player can hold on at least 29 rounds; and identify any optimal strategy's first two moves. - 2020-06-06 - At Most 43 Moves, At Least 29 - arXiv.org - - - http://arxiv.org/abs/2006.02353 - - - 2021-01-02 18:17:55 - arXiv: 2006.02353 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/2006.02353.pdf - - - 2021-01-02 18:17:57 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/2006.02353 - - - 2021-01-02 18:18:02 - 1 - text/html - - - journalArticle - - arXiv:2004.00377 [cs] - - - - - - Muller-Brockhausen - Matthias - - - - - Preuss - Mike - - - - - Plaat - Aske - - - - - - - - - Computer Science - Artificial Intelligence - - - A New Challenge: Approaching Tetris Link with AI - Decades of research have been invested in making computer programs for playing games such as Chess and Go. This paper focuses on a new game, Tetris Link, a board game that is still lacking any scientific analysis. Tetris Link has a large branching factor, hampering a traditional heuristic planning approach. We explore heuristic planning and two other approaches: Reinforcement Learning, Monte Carlo tree search. We document our approach and report on their relative performance in a tournament. Curiously, the heuristic approach is stronger than the planning/learning approaches. However, experienced human players easily win the majority of the matches against the heuristic planning AIs. We, therefore, surmise that Tetris Link is more difficult than expected. We offer our findings to the community as a challenge to improve upon. - 2020-04-01 - A New Challenge - arXiv.org - - - http://arxiv.org/abs/2004.00377 - - - 2021-01-02 18:18:26 - arXiv: 2004.00377 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/2004.00377.pdf - - - 2021-01-02 18:18:32 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/2004.00377 - - - 2021-01-02 18:18:38 - 1 - text/html - - - journalArticle - - arXiv:1511.08099 [cs] - - - - - - Cuayáhuitl - Heriberto - - - - - Keizer - Simon - - - - - Lemon - Oliver - - - - - - - - - Computer Science - Artificial Intelligence - - - - - Computer Science - Machine Learning - - - Strategic Dialogue Management via Deep Reinforcement Learning - Artificially intelligent agents equipped with strategic skills that can negotiate during their interactions with other natural or artificial agents are still underdeveloped. This paper describes a successful application of Deep Reinforcement Learning (DRL) for training intelligent agents with strategic conversational skills, in a situated dialogue setting. Previous studies have modelled the behaviour of strategic agents using supervised learning and traditional reinforcement learning techniques, the latter using tabular representations or learning with linear function approximation. In this study, we apply DRL with a high-dimensional state space to the strategic board game of Settlers of Catan---where players can offer resources in exchange for others and they can also reply to offers made by other players. Our experimental results report that the DRL-based learnt policies significantly outperformed several baselines including random, rule-based, and supervised-based behaviours. The DRL-based policy has a 53% win rate versus 3 automated players (`bots'), whereas a supervised player trained on a dialogue corpus in this setting achieved only 27%, versus the same 3 bots. This result supports the claim that DRL is a promising framework for training dialogue systems, and strategic agents with negotiation abilities. - 2015-11-25 - arXiv.org - - - http://arxiv.org/abs/1511.08099 - - - 2021-01-02 18:29:38 - arXiv: 1511.08099 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/1511.08099.pdf - - - 2021-01-02 18:29:43 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/1511.08099 - - - 2021-01-02 18:29:50 - 1 - text/html - - - conferencePaper - - - - - Applying Neural Networks and Genetic Programming to the Game Lost Cities - - - https://minds.wisconsin.edu/bitstream/handle/1793/79080/LydeenSpr18.pdf?sequence=1&isAllowed=y - - - - - attachment - LydeenSpr18.pdf - - - https://minds.wisconsin.edu/bitstream/handle/1793/79080/LydeenSpr18.pdf - - - 2021-06-12 17:03:24 - 3 - - - report - A summary of a dissertation on Azul - - - https://old.reddit.com/r/boardgames/comments/hxodaf/update_i_wrote_my_dissertation_on_azul/ - - - - - conferencePaper - - - - Ceramic: A research environment based on the multi-player strategic board game Azul - - - https://ipsj.ixsq.nii.ac.jp/ej/?action=repository_action_common_download&item_id=207669&item_no=1&attribute_id=1&file_no=1 - - - - - computerProgram - Ceramic: A research environment based on the multi-player strategic board game Azul - - - https://github.com/Swynfel/ceramic - - - - - report - Blokus Game Solver - - - https://digitalcommons.calpoly.edu/cpesp/290/ - - - - - conferencePaper - - - ISBN 978-1-4799-2198-0 978-1-4799-2199-7 - 2013 International Conference on Field-Programmable Technology (FPT) - DOI 10.1109/FPT.2013.6718426 - - - - - - - Kyoto, Japan - - - IEEE - - - - - - - Yoza - Takashi - - - - - Moriwaki - Retsu - - - - - Torigai - Yuki - - - - - Kamikubo - Yuki - - - - - Kubota - Takayuki - - - - - Watanabe - Takahiro - - - - - Fujimori - Takumi - - - - - Ito - Hiroyuki - - - - - Seo - Masato - - - - - Akagi - Kouta - - - - - Yamaji - Yuichiro - - - - - Watanabe - Minoru - - - - - - FPGA Blokus Duo Solver using a massively parallel architecture - 12/2013 - DOI.org (Crossref) - - - http://ieeexplore.ieee.org/document/6718426/ - - - 2021-06-28 14:38:57 - 494-497 - - - 2013 International Conference on Field-Programmable Technology (FPT) - - - - - attachment - Full Text - - - https://zero.sci-hub.se/2654/a4d3e713290066b6db7db1d9eedd194e/yoza2013.pdf#view=FitH - - - 2021-06-28 14:39:08 - 1 - application/pdf - - - conferencePaper - - - ISBN 978-1-4799-0565-2 978-1-4799-0562-1 978-1-4799-0563-8 - The 17th CSI International Symposium on Computer Architecture & Digital Systems (CADS 2013) - DOI 10.1109/CADS.2013.6714256 - - - - - - - Tehran, Iran - - - IEEE - - - - - - - Jahanshahi - Ali - - - - - Taram - Mohammad Kazem - - - - - Eskandari - Nariman - - - - - - Blokus Duo game on FPGA - 10/2013 - DOI.org (Crossref) - - - http://ieeexplore.ieee.org/document/6714256/ - - - 2021-06-28 14:39:04 - 149-152 - - - 2013 17th CSI International Symposium on Computer Architecture and Digital Systems (CADS) - - - - - attachment - Full Text - - - https://zero.sci-hub.se/3228/9ae6ca1efab5a2ebb63dd4e22a13bf04/jahanshahi2013.pdf#view=FitH - - - 2021-06-28 14:39:07 - 1 - application/pdf - - - journalArticle - - - The World Wide Web Conference - DOI 10.1145/3308558.3314131 - - - - Hsu - Chao-Chun - - - - - Chen - Yu-Hua - - - - - Chen - Zi-Yuan - - - - - Lin - Hsin-Yu - - - - - Huang - Ting-Hao 'Kenneth' - - - - - Ku - Lun-Wei + Tsan-sheng - - - - - Computer Science - Computation and Language - - - Dixit: Interactive Visual Storytelling via Term Manipulation - In this paper, we introduce Dixit, an interactive visual storytelling system that the user interacts with iteratively to compose a short story for a photo sequence. The user initiates the process by uploading a sequence of photos. Dixit first extracts text terms from each photo which describe the objects (e.g., boy, bike) or actions (e.g., sleep) in the photo, and then allows the user to add new terms or remove existing terms. Dixit then generates a short story based on these terms. Behind the scenes, Dixit uses an LSTM-based model trained on image caption data and FrameNet to distill terms from each image and utilizes a transformer decoder to compose a context-coherent story. Users change images or terms iteratively with Dixit to create the most ideal story. Dixit also allows users to manually edit and rate stories. The proposed procedure opens up possibilities for interpretable and controllable visual storytelling, allowing users to understand the story formation rationale and to intervene in the generation process. - 2019-05-13 - Dixit - arXiv.org - - - http://arxiv.org/abs/1903.02230 - - - 2021-06-28 14:40:29 - arXiv: 1903.02230 - 3531-3535 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/1903.02230.pdf - - - 2021-06-28 14:40:38 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/1903.02230 - - - 2021-06-28 14:40:43 - 1 - text/html - - - computerProgram - Dominion Simulator - - - https://dominionsimulator.wordpress.com/f-a-q/ - - - - - computerProgram - Dominion Simulator Source Code - - - https://github.com/mikemccllstr/dominionstats/ - - - - - blogPost - - - - Best and worst openings in Dominion - - - http://councilroom.com/openings - - - - - blogPost - - - - Optimal Card Ratios in Dominion - - - http://councilroom.com/optimal_card_ratios - - - - - blogPost - - - - Card Winning Stats on Dominion Server - - - http://councilroom.com/supply_win - - - - - forumPost - - - - Dominion Strategy Forum - - - http://forum.dominionstrategy.com/index.php - - - - - journalArticle - - arXiv:1811.11273 [cs] - - - - - - Bendekgey - Henry - - - - - - - - - Computer Science - Artificial Intelligence - - - Clustering Player Strategies from Variable-Length Game Logs in Dominion - We present a method for encoding game logs as numeric features in the card game Dominion. We then run the manifold learning algorithm t-SNE on these encodings to visualize the landscape of player strategies. By quantifying game states as the relative prevalence of cards in a player's deck, we create visualizations that capture qualitative differences in player strategies. Different ways of deviating from the starting game state appear as different rays in the visualization, giving it an intuitive explanation. This is a promising new direction for understanding player strategies across games that vary in length. - 2018-12-12 - arXiv.org - - - http://arxiv.org/abs/1811.11273 - - - 2021-06-28 14:43:21 - arXiv: 1811.11273 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/1811.11273.pdf - - - 2021-06-28 14:43:27 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/1811.11273 - - - 2021-06-28 14:43:31 - 1 - text/html - - - computerProgram - Hanabi Open Agent Dataset - - - https://github.com/aronsar/hoad - - - - - conferencePaper - - - - Hanabi Open Agent Dataset - - - https://dl.acm.org/doi/10.5555/3463952.3464188 - - - - - journalArticle - - arXiv:2010.02923 [cs] - - - - - - Gray - Jonathan - - - - - Lerer - Adam - - - - - Bakhtin - Anton - - - - - Brown - Noam - - - - - - - - - Computer Science - Artificial Intelligence - - - - - Computer Science - Machine Learning - - - - - Computer Science - Computer Science and Game Theory - - - Human-Level Performance in No-Press Diplomacy via Equilibrium Search - Prior AI breakthroughs in complex games have focused on either the purely adversarial or purely cooperative settings. In contrast, Diplomacy is a game of shifting alliances that involves both cooperation and competition. For this reason, Diplomacy has proven to be a formidable research challenge. In this paper we describe an agent for the no-press variant of Diplomacy that combines supervised learning on human data with one-step lookahead search via regret minimization. Regret minimization techniques have been behind previous AI successes in adversarial games, most notably poker, but have not previously been shown to be successful in large-scale games involving cooperation. We show that our agent greatly exceeds the performance of past no-press Diplomacy bots, is unexploitable by expert humans, and ranks in the top 2% of human players when playing anonymous games on a popular Diplomacy website. - 2021-05-03 - arXiv.org - - - http://arxiv.org/abs/2010.02923 - - - 2021-06-28 15:28:02 - arXiv: 2010.02923 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/2010.02923.pdf - - - 2021-06-28 15:28:18 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/2010.02923 - - - 2021-06-28 15:28:22 - 1 - text/html - - - journalArticle - - arXiv:1708.01503 [math] - - - - - - Akiyama - Rika - - - - - Abe - Nozomi - - - - - Fujita - Hajime - - - - - Inaba - Yukie - - - - - Hataoka - Mari - - - - - Ito - Shiori - - - - - Seita - Satomi - - - - - - - - - 55A20 (Primary), 05A99 (Secondary) - - - - - Mathematics - Combinatorics - - - - - Mathematics - Geometric Topology - - - - - Mathematics - History and Overview - - - Maximum genus of the Jenga like configurations - We treat the boundary of the union of blocks in the Jenga game as a surface with a polyhedral structure and consider its genus. We generalize the game and determine the maximum genus of the generalized game. - 2018-08-31 - arXiv.org - - - http://arxiv.org/abs/1708.01503 - - - 2021-06-28 15:28:12 - arXiv: 1708.01503 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/1708.01503.pdf - - - 2021-06-28 15:28:21 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/1708.01503 - - - 2021-06-28 15:28:24 - 1 - text/html - - - journalArticle - - arXiv:1905.08617 [cs] - - - - - - Bai - Chongyang - - - - - Bolonkin - Maksim - - - - - Burgoon - Judee - - - - - Chen - Chao - - - - - Dunbar - Norah - - - - - Singh - Bharat - - - - - Subrahmanian - V. S. - - - - - Wu - Zhe - - - - - - - - - Computer Science - Artificial Intelligence - - - - - Computer Science - Computer Vision and Pattern Recognition - - - Automatic Long-Term Deception Detection in Group Interaction Videos - Most work on automated deception detection (ADD) in video has two restrictions: (i) it focuses on a video of one person, and (ii) it focuses on a single act of deception in a one or two minute video. In this paper, we propose a new ADD framework which captures long term deception in a group setting. We study deception in the well-known Resistance game (like Mafia and Werewolf) which consists of 5-8 players of whom 2-3 are spies. Spies are deceptive throughout the game (typically 30-65 minutes) to keep their identity hidden. We develop an ensemble predictive model to identify spies in Resistance videos. We show that features from low-level and high-level video analysis are insufficient, but when combined with a new class of features that we call LiarRank, produce the best results. We achieve AUCs of over 0.70 in a fully automated setting. Our demo can be found at http://home.cs.dartmouth.edu/~mbolonkin/scan/demo/ - 2019-06-15 - arXiv.org - - - http://arxiv.org/abs/1905.08617 - - - 2021-06-28 15:32:49 - arXiv: 1905.08617 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/1905.08617.pdf - - - 2021-06-28 15:32:54 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/1905.08617 - - - 2021-06-28 15:32:58 - 1 - text/html - - - bookSection - - - 10068 - ISBN 978-3-319-50934-1 978-3-319-50935-8 - Computers and Games - - - - - - - Cham - - - Springer International Publishing - - - - - - - Plaat - Aske - - - - - Kosters - Walter - - - - - van den Herik - Jaap - - - - - - - - - Bi - Xiaoheng - - - - - Tanaka - Tetsuro - - - - - - Human-Side Strategies in the Werewolf Game Against the Stealth Werewolf Strategy - 2016 + AlphaZero for a Non-Deterministic Game + 11/2018 DOI.org (Crossref) - http://link.springer.com/10.1007/978-3-319-50935-8_9 + https://ieeexplore.ieee.org/document/8588490/ - 2021-06-28 15:32:54 - Series Title: Lecture Notes in Computer Science -DOI: 10.1007/978-3-319-50935-8_9 - 93-102 - - - attachment - Full Text - - - https://sci-hub.se/downloads/2019-01-26//f7/bi2016.pdf#view=FitH - - - 2021-06-28 15:33:08 - 1 - application/pdf - - - journalArticle - - arXiv:0804.0071 [math] - - - - - - Yao - Erlin - - - - - - - - 65C20 - - - 91-01 - - - - Mathematics - Probability - - - A Theoretical Study of Mafia Games - Mafia can be described as an experiment in human psychology and mass hysteria, or as a game between informed minority and uninformed majority. Focus on a very restricted setting, Mossel et al. [to appear in Ann. Appl. Probab. Volume 18, Number 2] showed that in the mafia game without detectives, if the civilians and mafias both adopt the optimal randomized strategy, then the two groups have comparable probabilities of winning exactly when the total player size is R and the mafia size is of order Sqrt(R). They also proposed a conjecture which stated that this phenomenon should be valid in a more extensive framework. In this paper, we first indicate that the main theorem given by Mossel et al. [to appear in Ann. Appl. Probab. Volume 18, Number 2] can not guarantee their conclusion, i.e., the two groups have comparable winning probabilities when the mafia size is of order Sqrt(R). Then we give a theorem which validates the correctness of their conclusion. In the last, by proving the conjecture proposed by Mossel et al. [to appear in Ann. Appl. Probab. Volume 18, Number 2], we generalize the phenomenon to a more extensive framework, of which the mafia game without detectives is only a special case. - 2008-04-01 - arXiv.org - - - http://arxiv.org/abs/0804.0071 - - - 2021-06-28 15:33:04 - arXiv: 0804.0071 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/0804.0071.pdf - - - 2021-06-28 15:33:07 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/0804.0071 - - - 2021-06-28 15:33:10 - 1 - text/html - - - bookSection - - - 11302 - ISBN 978-3-030-04178-6 978-3-030-04179-3 - Neural Information Processing - - - - - - - Cham - - - Springer International Publishing - - - - - - - Cheng - Long - - - - - Leung - Andrew Chi Sing - - - - - Ozawa - Seiichi - - - - - - - - - Zilio - Felipe - - - - - Prates - Marcelo - - - - - Lamb - Luis - - - - - - Neural Networks Models for Analyzing Magic: The Gathering Cards - 2018 - Neural Networks Models for Analyzing Magic - DOI.org (Crossref) - - - http://link.springer.com/10.1007/978-3-030-04179-3_20 - - - 2021-06-28 15:33:26 - Series Title: Lecture Notes in Computer Science -DOI: 10.1007/978-3-030-04179-3_20 - 227-239 - - - attachment - Submitted Version - - - https://arxiv.org/pdf/1810.03744 - - - 2021-06-28 15:33:36 - 1 - application/pdf - - - conferencePaper - - - - The Complexity of Deciding Legality of a Single Step of Magic: The Gathering - - - https://livrepository.liverpool.ac.uk/3029568/ - - - - - conferencePaper - - - - Magic: The Gathering in Common Lisp - - - https://vixra.org/abs/2001.0065 - - - - - computerProgram - Magic: The Gathering in Common Lisp - - - https://github.com/jeffythedragonslayer/maglisp - - - - - thesis - Mathematical programming and Magic: The Gathering - - - https://commons.lib.niu.edu/handle/10843/19194 - - - - - conferencePaper - - - - Deck Construction Strategies for Magic: The Gathering - - - https://www.doi.org/10.1685/CSC06077 - - - - - thesis - Deckbuilding in Magic: The Gathering Using a Genetic Algorithm - - - https://doi.org/11250/2462429 - - - - - report - Magic: The Gathering Deck Performance Prediction - - - http://cs229.stanford.edu/proj2012/HauPlotkinTran-MagicTheGatheringDeckPerformancePrediction.pdf - - - - - computerProgram - A constraint programming based solver for Modern Art - - - https://github.com/captn3m0/modernart - - - - - journalArticle - - arXiv:2103.00683 [cs] - - - - - - Haliem - Marina - - - - - Bonjour - Trevor - - - - - Alsalem - Aala - - - - - Thomas - Shilpa - - - - - Li - Hongyu - - - - - Aggarwal - Vaneet - - - - - Bhargava - Bharat - - - - - Kejriwal - Mayank - - - - - - - - - Computer Science - Artificial Intelligence - - - - - Computer Science - Machine Learning - - - Learning Monopoly Gameplay: A Hybrid Model-Free Deep Reinforcement Learning and Imitation Learning Approach - Learning how to adapt and make real-time informed decisions in dynamic and complex environments is a challenging problem. To learn this task, Reinforcement Learning (RL) relies on an agent interacting with an environment and learning through trial and error to maximize the cumulative sum of rewards received by it. In multi-player Monopoly game, players have to make several decisions every turn which involves complex actions, such as making trades. This makes the decision-making harder and thus, introduces a highly complicated task for an RL agent to play and learn its winning strategies. In this paper, we introduce a Hybrid Model-Free Deep RL (DRL) approach that is capable of playing and learning winning strategies of the popular board game, Monopoly. To achieve this, our DRL agent (1) starts its learning process by imitating a rule-based agent (that resembles the human logic) to initialize its policy, (2) learns the successful actions, and improves its policy using DRL. Experimental results demonstrate an intelligent behavior of our proposed agent as it shows high win rates against different types of agent-players. - 2021-02-28 - Learning Monopoly Gameplay - arXiv.org - - - http://arxiv.org/abs/2103.00683 - - - 2021-06-28 15:48:08 - arXiv: 2103.00683 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/2103.00683.pdf - - - 2021-06-28 15:48:19 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/2103.00683 - - - 2021-06-28 15:48:23 - 1 - text/html - - - conferencePaper - - - ISBN 978-0-7803-7203-0 - Proceedings 2001 IEEE International Symposium on Computational Intelligence in Robotics and Automation (Cat. No.01EX515) - DOI 10.1109/CIRA.2001.1013210 - - - - - - - Banff, Alta., Canada - - - IEEE - - - - - - - Yasumura - Y. - - - - - Oguchi - K. - - - - - Nitta - K. - - - - - - Negotiation strategy of agents in the MONOPOLY game - 2001 - DOI.org (Crossref) - - - http://ieeexplore.ieee.org/document/1013210/ - - - 2021-06-28 15:49:10 - 277-281 + 2021-12-08 07:06:31 + 116-121 - 2001 International Symposium on Computational Intelligence in Robotics and Automation + 2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) - - attachment - Full Text - - - https://moscow.sci-hub.se/3317/19346a5b777c1582800b51ee3a7cf5ed/negotiation-strategy-of-agents-in-the-monopoly-game.pdf#view=FitH - - - 2021-06-28 15:49:15 - 1 - application/pdf - - - conferencePaper - - - ISBN 978-1-4673-1194-6 978-1-4673-1193-9 978-1-4673-1192-2 - 2012 IEEE Conference on Computational Intelligence and Games (CIG) - DOI 10.1109/CIG.2012.6374168 - - - - - - - Granada, Spain - - - IEEE - - + + journalArticle + - Friberger - Marie Gustafsson + Silver + David - Togelius - Julian - - - - - - Generating interesting Monopoly boards from open data - 09/2012 - DOI.org (Crossref) - - - http://ieeexplore.ieee.org/document/6374168/ - - - 2021-06-28 15:49:18 - 288-295 - - - 2012 IEEE Conference on Computational Intelligence and Games (CIG) - - - - - attachment - Submitted Version - - - http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=81CA58D9ACCE8CA7412077093E520EFC?doi=10.1.1.348.6099&rep=rep1&type=pdf - - - 2021-06-28 15:49:32 - 1 - application/pdf - - - conferencePaper - - - ISBN 978-1-4244-5770-0 978-1-4244-5771-7 - Proceedings of the 2009 Winter Simulation Conference (WSC) - DOI 10.1109/WSC.2009.5429349 - - - - - - - Austin, TX, USA - - - IEEE - - - - - - - Friedman - Eric J. - - - - - Henderson - Shane G. - - - - - Byuen + Hubert Thomas - Gallardo - German Gutierrez - - - - - - Estimating the probability that the game of Monopoly never ends - 12/2009 - DOI.org (Crossref) - - - http://ieeexplore.ieee.org/document/5429349/ - - - 2021-06-28 15:49:23 - 380-391 - - - 2009 Winter Simulation Conference (WSC 2009) - - - - - attachment - Full Text - - - https://moscow.sci-hub.se/3233/bacac19e84c764b72c627d05f55c0ad9/friedman2009.pdf#view=FitH - - - 2021-06-28 15:49:32 - 1 - application/pdf - - - report - Learning to Play Monopoly with Monte Carlo Tree Search - - - https://project-archive.inf.ed.ac.uk/ug4/20181042/ug4_proj.pdf - - - - - conferencePaper - - - ISBN 978-1-72811-895-6 - TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) - DOI 10.1109/TENCON.2019.8929523 - - - - - - - Kochi, India - - - IEEE - - - - - - - Arun - Edupuganti + Schrittwieser + Julian - Rajesh - Harikrishna - - - - - Chakrabarti - Debarka - - - - - Cherala - Harikiran - - - - - George - Koshy - - - - - - Monopoly Using Reinforcement Learning - 10/2019 - DOI.org (Crossref) - - - https://ieeexplore.ieee.org/document/8929523/ - - - 2021-06-28 15:49:50 - 858-862 - - - TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) - - - - - attachment - Full Text - - - https://sci-hub.se/downloads/2020-04-10/35/arun2019.pdf?rand=60d9ef9f20b26#view=FitH - - - 2021-06-28 15:50:07 - 1 - application/pdf - - - report - A Markovian Exploration of Monopoly - - - https://pi4.math.illinois.edu/wp-content/uploads/2014/10/Gartland-Burson-Ferguson-Markovopoly.pdf - - - - - conferencePaper - - - - Learning to play Monopoly: A Reinforcement Learning approach - - - https://intelligence.csd.auth.gr/publication/conference-papers/learning-to-play-monopoly-a-reinforcement-learning-approach/ - - - - - presentation - What’s the Best Monopoly Strategy? - - - https://core.ac.uk/download/pdf/48614184.pdf - - - - - journalArticle - - - - - - Nakai - Kenichiro - - - - - Takenaga - Yasuhiko - - - - - - NP-Completeness of Pandemic - 2012 - en - DOI.org (Crossref) - - - https://www.jstage.jst.go.jp/article/ipsjjip/20/3/20_723/_article - - - 2021-06-28 15:59:47 - 723-726 - - - 20 - Journal of Information Processing - DOI 10.2197/ipsjjip.20.723 - 3 - Journal of Information Processing - ISSN 1882-6652 - - - attachment - Full Text - - - https://www.jstage.jst.go.jp/article/ipsjjip/20/3/20_723/_pdf - - - 2021-06-28 15:59:50 - 1 - application/pdf - - - thesis - On Solving Pentago - - - http://www.ke.tu-darmstadt.de/lehre/arbeiten/bachelor/2011/Buescher_Niklas.pdf - - - - - journalArticle - - - arXiv:1906.02330 [cs, stat] - - - - - - - Serrino - Jack - - - - - Kleiman-Weiner - Max - - - - - Parkes - David C. - - - - - Tenenbaum - Joshua B. - - - - - - - - - Computer Science - Machine Learning - - - - - Statistics - Machine Learning - - - - - Computer Science - Multiagent Systems - - - Finding Friend and Foe in Multi-Agent Games - Recent breakthroughs in AI for multi-agent games like Go, Poker, and Dota, have seen great strides in recent years. Yet none of these games address the real-life challenge of cooperation in the presence of unknown and uncertain teammates. This challenge is a key game mechanism in hidden role games. Here we develop the DeepRole algorithm, a multi-agent reinforcement learning agent that we test on The Resistance: Avalon, the most popular hidden role game. DeepRole combines counterfactual regret minimization (CFR) with deep value networks trained through self-play. Our algorithm integrates deductive reasoning into vector-form CFR to reason about joint beliefs and deduce partially observable actions. We augment deep value networks with constraints that yield interpretable representations of win probabilities. These innovations enable DeepRole to scale to the full Avalon game. Empirical game-theoretic methods show that DeepRole outperforms other hand-crafted and learned agents in five-player Avalon. DeepRole played with and against human players on the web in hybrid human-agent teams. We find that DeepRole outperforms human players as both a cooperator and a competitor. - 2019-06-05 - arXiv.org - - - http://arxiv.org/abs/1906.02330 - - - 2021-06-28 16:00:28 - arXiv: 1906.02330 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/1906.02330.pdf - - - 2021-06-28 16:00:35 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/1906.02330 - - - 2021-06-28 16:00:38 - 1 - text/html - - - thesis - A Mathematical Analysis of the Game of Santorini - - - https://openworks.wooster.edu/independentstudy/8917/ - - - - - computerProgram - A Mathematical Analysis of the Game of Santorini - - - https://github.com/carsongeissler/SantoriniIS - - - - - report - The complexity of Scotland Yard - - - https://eprints.illc.uva.nl/id/eprint/193/1/PP-2006-18.text.pdf - - - - - conferencePaper - - - ISBN 978-1-4799-3547-5 - 2014 IEEE Conference on Computational Intelligence and Games - DOI 10.1109/CIG.2014.6932907 - - - - - - - Dortmund, Germany - - - IEEE - - - - - - - Szubert - Marcin - - - - - Jaskowski - Wojciech - - - - - - Temporal difference learning of N-tuple networks for the game 2048 - 8/2014 - DOI.org (Crossref) - - - http://ieeexplore.ieee.org/document/6932907/ - - - 2021-06-28 16:09:20 - 1-8 - - - 2014 IEEE Conference on Computational Intelligence and Games (CIG) - - - - - attachment - Submitted Version - - - https://www.cs.put.poznan.pl/mszubert/pub/szubert2014cig.pdf - - - 2021-06-28 16:09:26 - 1 - application/pdf - - - journalArticle - - arXiv:1501.03837 [cs] - - - - - - Abdelkader - Ahmed - - - - - Acharya - Aditya - - - - - Dasler - Philip - - - - - - - - - Computer Science - Computational Complexity - - - - F.2.2 - - On the Complexity of Slide-and-Merge Games - We study the complexity of a particular class of board games, which we call `slide and merge' games. Namely, we consider 2048 and Threes, which are among the most popular games of their type. In both games, the player is required to slide all rows or columns of the board in one direction to create a high value tile by merging pairs of equal tiles into one with the sum of their values. This combines features from both block pushing and tile matching puzzles, like Push and Bejeweled, respectively. We define a number of natural decision problems on a suitable generalization of these games and prove NP-hardness for 2048 by reducing from 3SAT. Finally, we discuss the adaptation of our reduction to Threes and conjecture a similar result. - 2015-01-15 - arXiv.org - - - http://arxiv.org/abs/1501.03837 - - - 2021-06-28 16:09:34 - arXiv: 1501.03837 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/1501.03837.pdf - - - 2021-06-28 16:09:48 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/1501.03837 - - - 2021-06-28 16:09:52 - 1 - text/html - - - journalArticle - - - DOI 10.4230/LIPICS.FUN.2016.1 - - - - - - - Abdelkader - Ahmed - - - - - Acharya - Aditya - - - - - Dasler - Philip - - - - - - - - - Herbstritt - Marc - - - - - - - 000 Computer science, knowledge, general works - - - - - Computer Science - - - 2048 Without New Tiles Is Still Hard - 2016 - en - DOI.org (Datacite) - - - http://drops.dagstuhl.de/opus/volltexte/2016/5885/ - - - 2021-06-28 16:09:58 - Artwork Size: 14 pages -Medium: application/pdf -Publisher: Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH, Wadern/Saarbruecken, Germany - 14 pages - - - conferencePaper - - - - MDA: A Formal Approach to Game Design and Game Research - - - https://aaai.org/Library/Workshops/2004/ws04-04-001.php - - - - - conferencePaper - - - 6 - ISBN 2342-9666 - Think Design Play - - - - - DiGRA/Utrecht School of the Arts - - - Exploring anonymity in cooperative board games - This study was done as a part of a larger research project where the interest was on exploring if and how gameplay design could give informative principles to the design of educational activities. The researchers conducted a series of studies trying to map game mechanics that had the special quality of being inclusive, i.e., playable by a diverse group of players. This specific study focused on designing a cooperative board game with the goal of implementing anonymity as a game mechanic. Inspired by the gameplay design patterns methodology (Björk & Holopainen 2005a; 2005b; Holopainen & Björk 2008), mechanics from existing cooperative board games were extracted and analyzed in order to inform the design process. The results from prototyping and play testing indicated that it is possible to implement anonymous actions in cooperative board games and that this mechanic made rather unique forms of gameplay possible. These design patterns can be further developed in order to address inclusive educational practices. - January 2011 - - - http://www.digra.org/digital-library/publications/exploring-anonymity-in-cooperative-board-games/ - - - - - 2011 DiGRA International Conference - - - - - journalArticle - - arXiv:2107.07630 [cs] - - - - - - Siu - Ho Chit - - - - - Pena - Jaime D. - - - - - Chang - Kimberlee C. - - - - - Chen - Edenna - - - - - Zhou - Yutai - - - - - Lopez - Victor J. - - - - - Palko - Kyle - - - - - Allen - Ross E. - - - - - - - - - Computer Science - Artificial Intelligence - - - - - Computer Science - Human-Computer Interaction - - - Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi - Deep reinforcement learning has generated superhuman AI in competitive games such as Go and StarCraft. Can similar learning techniques create a superior AI teammate for human-machine collaborative games? Will humans prefer AI teammates that improve objective team performance or those that improve subjective metrics of trust? In this study, we perform a single-blind evaluation of teams of humans and AI agents in the cooperative card game Hanabi, with both rule-based and learning-based agents. In addition to the game score, used as an objective metric of the human-AI team performance, we also quantify subjective measures of the human's perceived performance, teamwork, interpretability, trust, and overall preference of AI teammate. We find that humans have a clear preference toward a rule-based AI teammate (SmartBot) over a state-of-the-art learning-based AI teammate (Other-Play) across nearly all subjective metrics, and generally view the learning-based agent negatively, despite no statistical difference in the game score. This result has implications for future AI design and reinforcement learning benchmarking, highlighting the need to incorporate subjective metrics of human-AI teaming rather than a singular focus on objective task performance. - 2021-07-19 - arXiv.org - - - http://arxiv.org/abs/2107.07630 - - - 2021-07-24 06:30:44 - arXiv: 2107.07630 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/2107.07630.pdf - - - 2021-07-24 06:31:01 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/2107.07630 - - - 2021-07-24 06:31:06 - 1 - text/html - - - journalArticle - - - - - - Litwiller - Bonnie H. - - - - - Duncan - David R. - - - - - Probabilites In Yahtzee - Teachers of units in probability are often interested in providing examples of probabilistic situations in a nonclassroom setting. Games are a rich source of such probabilities. Many people enjoy playing a commercial game called Yahtzee. A Yahtzee player receives points for achieving various specified numerical combinations of five dice during the three rolls that constitute a turn. - 12/1982 - DOI.org (Crossref) - - - https://pubs.nctm.org/view/journals/mt/75/9/article-p751.xml - - - 2021-07-24 07:53:57 - 751-754 - - - 75 - The Mathematics Teacher - DOI 10.5951/MT.75.9.0751 - 9 - MT - ISSN 0025-5769, 2330-0582 - - - presentation - - - - - Verhoeff - Tom - - - - - Optimal Solitaire Yahtzee Strategies - - - http://www.yahtzee.org.uk/optimal_yahtzee_TV.pdf - - - - - journalArticle - - - - - - - - Bonarini - Andrea - - - - - Lazaric - Alessandro - - - - - Restelli - Marcello - - - - - Yahtzee: a Large Stochastic Environment for RL Benchmarks - Yahtzee is a game that is regularly played by more than 100 million people in the world. We -propose a simplified version of Yahtzee as a benchmark for RL algorithms. We have already -used it for this purpose, and an implementation is available. - - - http://researchers.lille.inria.fr/~lazaric/Webpage/PublicationsByTopic_files/bonarini2005yahtzee.pdf - - - 1 - - - thesis - - - KTH, School of Computer Science and Communication (CSC) - - - - - - - Serra - Andreas - - - - - Niigata - Kai Widell - - - - - Optimal Yahtzee performance in multi-player games - Yahtzee is a game with a moderately large search space, dependent on the factor of luck. This makes it not quite trivial to implement an optimal strategy for it. Using the optimal strategy for single-player -use, comparisons against other algorithms are made and the results are analyzed for hints on what it could take to make an algorithm that could beat the single-player optimal strategy. - April 12, 2013 - en - http://www.diva-portal.org/smash/get/diva2:668705/FULLTEXT01.pdf - - - https://www.csc.kth.se/utbildning/kth/kurser/DD143X/dkand13/Group4Per/report/12-serra-widell-nigata.pdf - - - 17 - Independent thesis Basic level (degree of Bachelor) - - - manuscript - - - - - Verhoeff - Tom - - - - - How to Maximize Your Score in Solitaire Yahtzee - Yahtzee is a well-known game played with five dice. Players take turns at assembling and scoring dice patterns. The player with the highest score wins. Solitaire Yahtzee is a single-player version of Yahtzee aimed at maximizing one’s score. A strategy for playing Yahtzee determines which choice to make in each situation of the game. We show that the maximum expected score over all Solitaire Yahtzee strategies is 254.5896. . . . - en - - - http://www-set.win.tue.nl/~wstomv/misc/yahtzee/yahtzee-report-unfinished.pdf - - - 18 - Incomplete Draft - - - thesis - - - Yale University, Department of Computer Science - - - - - - - Vasseur - Philip - - - - - Using Deep Q-Learning to Compare Strategy Ladders of Yahtzee - “Bots” playing games is not a new concept, -likely going back to the first video games. However, -there has been a new wave recently using machine -learning to learn to play games at a near optimal -level - essentially using neural networks to “solve” -games. Depending on the game, this can be relatively -straight forward using supervised learning. However, -this requires having data for optimal play, which is -often not possible due to the sheer complexity of many -games. For example, solitaire Yahtzee has this data -available, but two player Yahtzee does not due to the -massive state space. A recent trend in response to this -started with Google Deep Mind in 2013, who used Deep -Reinforcement Learning to play various Atari games -[4]. -This project will apply Deep Reinforcement Learning -(specifically Deep Q-Learning) and measure how an -agent learns to play Yahtzee in the form of a strategy -ladder. A strategy ladder is a way of looking at how -the performance of an AI varies with the computational -resources it uses. Different sets of rules changes how the -the AI learns which varies the strategy ladder itself. This -project will vary the upper bonus threshold and then -attempt to measure how “good” the various strategy -ladders are - in essence attempting to find the set of -rules which creates the “best” version of Yahtzee. We -assume/expect that there is some correlation between -strategy ladders for AI and strategy ladders for human, -meaning that a game with a “good” strategy ladder for -an AI indicates that game is interesting and challenging -for humans. - December 12, 2019 - en - - - https://raw.githubusercontent.com/philvasseur/Yahtzee-DQN-Thesis/dcf2bfe15c3b8c0ff3256f02dd3c0aabdbcbc9bb/webpage/final_report.pdf - - - 12 - - - report - - - KTH Royal Institute Of Technology Computer Science And Communication - - - Defensive Yahtzee - In this project an algorithm has been created that plays Yahtzee using rule -based heuristics. The focus is getting a high lowest score and a high 10th -percentile. All rules of Yahtzee and the probabilities for each combination -have been studied and based on this each turn is optimized to get a -guaranteed decent high score. The algorithm got a lowest score of 79 and a -10th percentile of 152 when executed 100 000 times. - https://www.diva-portal.org/smash/get/diva2:817838/FULLTEXT01.pdf - - - http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168668 - - - 22 - - - report - - - - - Glenn - James - - - - - An Optimal Strategy for Yahtzee - - - http://www.cs.loyola.edu/~jglenn/research/optimal_yahtzee.pdf - - - - - presentation - - - - - Middlebury College - - - - - - - - - R. Teal Witter - - - - - Alex Lyford - - - - - - Applications of Graph Theory and Probability in the Board Game Ticket to Ride - January 16, 2020 - - - https://www.rtealwitter.com/slides/2020-JMM.pdf - - - - - attachment - Full Text - - - https://www.rtealwitter.com/slides/2020-JMM.pdf - - - 2021-07-24 08:18:37 - 1 - application/pdf - - - journalArticle - - arXiv:1511.08099 [cs] - - - - - - Cuayáhuitl - Heriberto - - - - - Keizer - Simon - - - - - Lemon - Oliver - - - - - - - - - Computer Science - Artificial Intelligence - - - - - Computer Science - Machine Learning - - - Strategic Dialogue Management via Deep Reinforcement Learning - Artificially intelligent agents equipped with strategic skills that can negotiate during their interactions with other natural or artificial agents are still underdeveloped. This paper describes a successful application of Deep Reinforcement Learning (DRL) for training intelligent agents with strategic conversational skills, in a situated dialogue setting. Previous studies have modelled the behaviour of strategic agents using supervised learning and traditional reinforcement learning techniques, the latter using tabular representations or learning with linear function approximation. In this study, we apply DRL with a high-dimensional state space to the strategic board game of Settlers of Catan---where players can offer resources in exchange for others and they can also reply to offers made by other players. Our experimental results report that the DRL-based learnt policies significantly outperformed several baselines including random, rule-based, and supervised-based behaviours. The DRL-based policy has a 53% win rate versus 3 automated players (`bots'), whereas a supervised player trained on a dialogue corpus in this setting achieved only 27%, versus the same 3 bots. This result supports the claim that DRL is a promising framework for training dialogue systems, and strategic agents with negotiation abilities. - 2015-11-25 - arXiv.org - - - http://arxiv.org/abs/1511.08099 - - - 2021-07-24 08:23:51 - arXiv: 1511.08099 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/1511.08099.pdf - - - 2021-07-24 08:23:57 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/1511.08099 - - - 2021-07-24 08:24:01 - 1 - text/html - - - conferencePaper - - - ISBN 978-92-837-2336-3 - 14th NATO Operations Research and Analysis (OR&A) Conference: Emerging and Disruptive Technology - DOI 10.14339/STO-MP-SAS-OCS-ORA-2020-WCM-01-PDF - - - - NATO - - - - - - Christoffer Limér - - - - - Erik Kalmér - - - - - Mika Cohen - - - - - - Monte Carlo Tree Search for Risk - 2/16/2021 - en - AC/323(SAS-ACT)TP/1017 - - - https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-SAS-OCS-ORA-2020/MP-SAS-OCS-ORA-2020-WCM-01.pdf - - - - - attachment - Full Text - - - https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-SAS-OCS-ORA-2020/MP-SAS-OCS-ORA-2020-WCM-01.pdf - - - 2021-07-24 08:34:15 - 1 - application/pdf - - - presentation - - - - - Christoffer Limér - - - - - Erik Kalmér - - - - - - Wargaming with Monte-Carlo Tree Search - 2/16/2021 - en - - - https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-SAS-OCS-ORA-2020/MP-SAS-OCS-ORA-2020-WCM-01P.pdf - - - - - attachment - Full Text - - - https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-SAS-OCS-ORA-2020/MP-SAS-OCS-ORA-2020-WCM-01P.pdf - - - 2021-07-24 08:35:04 - 1 - application/pdf - - - journalArticle - - arXiv:1910.04376 [cs] - - - - - - Zha - Daochen + Antonoglou + Ioannis Lai - Kwei-Herng + Matthew - Cao - Yuanpu + Guez + Arthur - Huang - Songyi + Lanctot + Marc - Wei - Ruzhe + Sifre + Laurent - Guo - Junyu + Kumaran + Dharshan - Hu - Xia + Graepel + Thore + + + + + Lillicrap + Timothy + + + + + Simonyan + Karen + + + + + Hassabis + Demis - - - - - Computer Science - Artificial Intelligence - - - RLCard: A Toolkit for Reinforcement Learning in Card Games - RLCard is an open-source toolkit for reinforcement learning research in card games. It supports various card environments with easy-to-use interfaces, including Blackjack, Leduc Hold'em, Texas Hold'em, UNO, Dou Dizhu and Mahjong. The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with multiple agents, large state and action space, and sparse reward. In this paper, we provide an overview of the key components in RLCard, a discussion of the design principles, a brief introduction of the interfaces, and comprehensive evaluations of the environments. The codes and documents are available at https://github.com/datamllab/rlcard - 2020-02-14 - RLCard - arXiv.org - - - http://arxiv.org/abs/1910.04376 - - - 2021-07-24 08:40:55 - arXiv: 1910.04376 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/1910.04376.pdf - - - 2021-07-24 08:40:59 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/1910.04376 - - - 2021-07-24 08:41:03 - 1 - text/html - - - journalArticle - - arXiv:2009.12065 [cs] - - - - - - Gaina - Raluca D. - - - - - Balla - Martin - - - - - Dockhorn - Alexander - - - - - Montoliu - Raul - - - - - Perez-Liebana - Diego - - - - - - - - - Computer Science - Artificial Intelligence - - - Design and Implementation of TAG: A Tabletop Games Framework - This document describes the design and implementation of the Tabletop Games framework (TAG), a Java-based benchmark for developing modern board games for AI research. TAG provides a common skeleton for implementing tabletop games based on a common API for AI agents, a set of components and classes to easily add new games and an import module for defining data in JSON format. At present, this platform includes the implementation of seven different tabletop games that can also be used as an example for further developments. Additionally, TAG also incorporates logging functionality that allows the user to perform a detailed analysis of the game, in terms of action space, branching factor, hidden information, and other measures of interest for Game AI research. The objective of this document is to serve as a central point where the framework can be described at length. TAG can be downloaded at: https://github.com/GAIGResearch/TabletopGames - 2020-09-25 - Design and Implementation of TAG - arXiv.org - - - http://arxiv.org/abs/2009.12065 - - - 2021-07-24 08:41:01 - arXiv: 2009.12065 - - - attachment - arXiv Fulltext PDF - - - https://arxiv.org/pdf/2009.12065.pdf - - - 2021-07-24 08:41:07 - 1 - application/pdf - - - attachment - arXiv.org Snapshot - - - https://arxiv.org/abs/2009.12065 - - - 2021-07-24 08:41:11 - 1 - text/html - - - computerProgram - - - - - Adam Stelmaszczyk - - - - - Game Tree Search Algorithms - C++ library for AI bot programming. - 2015 - Game Tree Search Algorithms - - - https://github.com/AdamStelmaszczyk/gtsa - - - C++ - - - computerProgram - - - - - Raluca D. Gaina - - - - - Martin Balla - - - - - Alexander Dockhorn - - - - - Raul Montoliu - - - - - Diego Perez-Liebana - - - - - TAG: Tabletop Games Framework - The Tabletop Games Framework (TAG) is a Java-based benchmark for developing modern board games for AI research. TAG provides a common skeleton for implementing tabletop games based on a common API for AI agents, a set of components and classes to easily add new games and an import module for defining data in JSON format. At present, this platform includes the implementation of seven different tabletop games that can also be used as an example for further developments. Additionally, TAG also incorporates logging functionality that allows the user to perform a detailed analysis of the game, in terms of action space, branching factor, hidden information, and other measures of interest for Game AI research. - - - https://github.com/GAIGResearch/TabletopGames - - - MIT License - Java - - - thesis - - - Örebro University, School of Science and Technology. - - - - - - - Nguyen, Van Hoa - - - - - A Graphical User Interface For The Hanabi Challenge Benchmark - This report will describe the development of the Graphical User Interface (GUI) forthe Hanabi Challenge Benchmark. The benchmark is based on the popular cardgame Hanabi and presents itself as a new research frontier in artificial intelligencefor cooperative multi-agent challenges. The project’s intentions and goals are tointerpret and visualize the data output from the benchmark to give us a better understandingof it.A GUI was then developed by using knowledge within theory of mind in combinationwith theories within human-computer interaction. The results of this project wereevaluated through a small-scale usability test. Users of different ages, gender andlevels of computer knowledge tested the application and through a questionnaire,the quality of the GUI was assessed. - - - http://oru.diva-portal.org/smash/record.jsf?pid=diva2%3A1597503 - - - - - journalArticle - - - - - - Osawa - Hirotaka - - - - - Kawagoe - Atsushi - - - - - Sato - Eisuke - - - - - Kato - Takuya - - - - - - Emergence of Cooperative Impression With Self-Estimation, Thinking Time, and Concordance of Risk Sensitivity in Playing Hanabi - The authors evaluate the extent to which a user’s impression of an AI agent can be improved by giving the agent the ability of self-estimation, thinking time, and coordination of risk tendency. The authors modified the algorithm of an AI agent in the cooperative game Hanabi to have all of these traits, and investigated the change in the user’s impression by playing with the user. The authors used a self-estimation task to evaluate the effect that the ability to read the intention of a user had on an impression. The authors also show thinking time of an agent influences impression for an agent. The authors also investigated the relationship between the concordance of the risk-taking tendencies of players and agents, the player’s impression of agents, and the game experience. The results of the self-estimation task experiment showed that the more accurate the estimation of the agent’s self, the more likely it is that the partner will perceive humanity, affinity, intelligence, and communication skills in the agent. The authors also found that an agent that changes the length of thinking time according to the priority of action gives the impression that it is smarter than an agent with a normal thinking time when the player notices the difference in thinking time or an agent that randomly changes the thinking time. The result of the experiment regarding concordance of the risk-taking tendency shows that influence player’s impression toward agents. These results suggest that game agent designers can improve the player’s disposition toward an agent and the game experience by adjusting the agent’s self-estimation level, thinking time, and risk-taking tendency according to the player’s personality and inner state during the game. - 2021-10-12 + A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play + 2018-12-07 + en DOI.org (Crossref) - https://www.frontiersin.org/articles/10.3389/frobt.2021.658348/full + https://www.science.org/doi/10.1126/science.aar6404 - 2021-11-24 07:14:38 - 658348 + 2021-12-08 07:07:12 + 1140-1144 - - 8 - Frontiers in Robotics and AI - DOI 10.3389/frobt.2021.658348 - Front. Robot. AI - ISSN 2296-9144 + + 362 + Science + DOI 10.1126/science.aar6404 + 6419 + Science + ISSN 0036-8075, 1095-9203 - - attachment - Full Text - - - https://www.frontiersin.org/articles/10.3389/frobt.2021.658348/pdf - - - 2021-11-24 07:15:06 - 1 - application/pdf - - + 2048 - - - - - - - - - - - - - Accessibility - - - - - Azul - - - - - - Blokus - - - + + + + + + + + + + + Accessibility + + + + + Azul + + + + + + Blokus + + + + + Carcassonne - + Diplomacy + + + - - - - + Dixit - + - + Dominion - - - - - - + + + + + + - + Frameworks - - - + + + + + + Game Design + + - Game Design - - + General Gameplay + + + + - + Hanabi - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + - + Hive - + Jenga - + Kingdomino @@ -8016,183 +8460,183 @@ guaranteed decent high score. The algorithm got a lowest score of 79 and a - + Lost Cities - + Mafia - - - + + + - + Magic: The Gathering - - - - - - - - - - - + + + + + + + + + + + - + Mobile Games - + Modern Art: The card game - + Monopoly - - - - - - - - + + + + + + + + - + Monopoly Deal - + Nmbr9 - + - + Pandemic - + Patchwork - - + + - + Pentago - + Quixo - + Race for the Galaxy - + Resistance: Avalon - - RISK - - - - - - - - - - - - - - Santorini - - - + RISK + + + + + + + + + + + + + + Santorini + + + + Scotland Yard - + Secret Hitler - + Set - + - + Settlers of Catan - - - - - - - - - - + + + - + + + + + + + + - + Shobu - + - + Terra Mystica - + Tetris Link Ticket to Ride - - - + + + - + Ultimate Tic-Tac-Toe - + UNO - + Yahtzee - - - - - - - - + + + + + + + +