84 lines
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84 lines
9.1 KiB
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<!DOCTYPE NETSCAPE-Bookmark-file-1>
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<TITLE>Bookmarks</TITLE>
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<H1>Bookmarks Menu</H1>
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<DL>
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<DT><A HREF="http://ieeexplore.ieee.org/document/6932861/">The effectiveness of persuasion in The Settlers of Catan</A>
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<DT><A HREF="https://doi.org/10.1109%2Fcig.2014.6932861">The effectiveness of persuasion in The Settlers of Catan</A>
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<DT><A HREF="https://doi.org/10.4018%2Fijgcms.2018040103">Avoiding Revenge Using Optimal Opponent Ranking Strategy in the Board Game Catan</A>
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<DT><A HREF="https://doi.org/10.1109%2Fcig.2014.6932884">Game strategies for The Settlers of Catan</A>
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<DT><A HREF="https://doi.org/10.1007%2F978-3-642-12993-3_3">Monte-Carlo Tree Search in Settlers of Catan</A>
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<DT><A HREF="https://doi.org/10.1007%2F978-3-030-14174-5_16">Deep Reinforcement Learning in Strategic Board Game Environments</A>
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<DT><A HREF="https://doi.org/10.1057%2Fjors.1990.2">Mini-Risk: Strategies for a Simplified Board Game</A>
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<DT><A HREF="https://doi.org/10.1145%2F508791.508904">Learning the risk board game with classifier systems</A>
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<DT><A HREF="https://doi.org/10.1080%2F0025570x.1997.11996573">Markov Chains and the RISK Board Game</A>
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<DT><A HREF="https://doi.org/10.1080%2F0025570x.2003.11953165">Markov Chains for the RISK Board Game Revisited</A>
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<DT><A HREF="https://doi.org/10.1109%2Ftevc.2005.856211">Planning an Endgame Move Set for the Game RISK: A Comparison of Search Algorithms</A>
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<DT><A HREF="https://doi.org/10.1109%2Fcig.2018.8490419">Monte Carlo Methods for the Game Kingdomino</A>
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<DT><A HREF="https://doi.org/10.4169%2Fmath.mag.88.5.323">How to Make the Perfect Fireworks Display: Two Strategies forHanabi</A>
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<DT><A HREF="https://doi.org/10.1109%2Fcec.2017.7969465">Evaluating and modelling Hanabi-playing agents</A>
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<DT><A HREF="https://doi.org/10.1016%2Fj.artint.2019.103216">The Hanabi challenge: A new frontier for AI research</A>
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<DT><A HREF="https://doi.org/10.1109%2Fcig.2019.8848008">The 2018 Hanabi competition</A>
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<DT><A HREF="https://doi.org/10.1109%2Fcig.2019.8847944">Diverse Agents for Ad-Hoc Cooperation in Hanabi</A>
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<DT><A HREF="https://doi.org/10.1080%2F0025570x.1972.11976187">Monopoly as a Markov Process</A>
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<DT><A HREF="https://doi.org/10.1109%2Ftciaig.2012.2204883">Ensemble Determinization in Monte Carlo Tree Search for the Imperfect Information Card Game Magic: The Gathering</A>
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<DT><A HREF="https://doi.org/10.1080%2F07468342.2000.11974103">Optimal Card-Collecting Strategies for Magic: The Gathering</A>
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<DT><A HREF="https://doi.org/10.1109%2Fcig.2009.5286501">Monte Carlo search applied to card selection in Magic: The Gathering</A>
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<DT><A HREF="https://doi.org/10.1007%2F978-3-642-13122-6_15">UNO Is Hard, Even for a Single Player</A>
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<DT><A HREF="https://doi.org/10.1016%2Fj.ipl.2020.105995">QUIXO is EXPTIME-complete</A>
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<DT><A HREF="https://doi.org/10.1007%2F978-3-319-61030-6_27">SCOUT: A Case-Based Reasoning Agent for Playing Race for the Galaxy</A>
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<DT><A HREF="https://doi.org/10.4169%2Fmath.mag.85.2.083">Game, Set, Math</A>
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<DT><A HREF="https://doi.org/10.1080%2F00029890.2018.1412661">The Joy of SET</A>
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<DT><A HREF="https://doi.org/10.1007%2F978-3-662-46742-8_11">Implementation of Artificial Intelligence with 3 Different Characters of AI Player on “Monopoly Deal” Computer Game</A>
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<DT><A HREF="https://doi.org/10.1007%2F978-3-642-17928-0_23">Nearly Optimal Computer Play in Multi-player Yahtzee</A>
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<DT><A HREF="https://doi.org/10.1109%2Fcig.2007.368089">Computer Strategies for Solitaire Yahtzee</A>
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<DT><A HREF="https://doi.org/10.1111%2F1468-0394.00160">Modeling expert problem solving in a game of chance: a Yahtzeec case study</A>
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<DT><A HREF="https://doi.org/10.1007%2F978-3-319-50935-8_8">Systematic Selection of N-Tuple Networks for 2048</A>
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<DT><A HREF="https://doi.org/10.1109%2Ftaai.2016.7880154">Systematic selection of N-tuple networks with consideration of interinfluence for game 2048</A>
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<DT><A HREF="https://doi.org/10.1109%2Fcig.2014.6932920">An investigation into 2048 AI strategies</A>
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<DT><A HREF="http://arxiv.org/abs/2006.04635v2">Learning to Play No-Press Diplomacy with Best Response Policy Iteration</A>
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<DT><A HREF="http://arxiv.org/abs/1909.02128v2">No Press Diplomacy: Modeling Multi-Agent Gameplay</A>
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<DT><A HREF="http://arxiv.org/abs/1902.06996v1">Agent Madoff: A Heuristic-Based Negotiation Agent For The Diplomacy Strategy Game</A>
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<DT><A HREF="http://arxiv.org/abs/1807.04458v2">Monte Carlo Methods for the Game Kingdomino</A>
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<DT><A HREF="http://arxiv.org/abs/1909.02849v3">NP-completeness of the game Kingdomino</A>
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<DT><A HREF="http://arxiv.org/abs/1912.02318v1">Improving Policies via Search in Cooperative Partially Observable Games</A>
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<DT><A HREF="http://arxiv.org/abs/1603.01911v3">Hanabi is NP-hard, Even for Cheaters who Look at Their Cards</A>
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<DT><A HREF="http://arxiv.org/abs/2004.13710v2">Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi</A>
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<DT><A HREF="http://arxiv.org/abs/2004.13291v1">Evaluating the Rainbow DQN Agent in Hanabi with Unseen Partners</A>
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<DT><A HREF="http://arxiv.org/abs/2003.05119v1">Magic: the Gathering is as Hard as Arithmetic</A>
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<DT><A HREF="http://arxiv.org/abs/1904.09828v2">Magic: The Gathering is Turing Complete</A>
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<DT><A HREF="http://arxiv.org/abs/1810.03744v1">Neural Networks Models for Analyzing Magic: the Gathering Cards</A>
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<DT><A HREF="https://doi.org/10.1145%2F3396474.3396492">Using Tabu Search Algorithm for Map Generation in the Terra Mystica Tabletop Game</A>
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<DT><A HREF="http://arxiv.org/abs/1009.1031v3">A mathematical model of the Mafia game</A>
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<DT><A HREF="http://arxiv.org/abs/1003.2851v3">The complexity of UNO</A>
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<DT><A HREF="http://arxiv.org/abs/1603.00928v1">Trainyard is NP-Hard</A>
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<DT><A HREF="http://arxiv.org/abs/1505.04274v1">Threes!, Fives, 1024!, and 2048 are Hard</A>
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<DT><A HREF="http://arxiv.org/abs/1804.07396v1">Making Change in 2048</A>
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<DT><A HREF="http://arxiv.org/abs/1804.07393v2">Analysis of the Game "2048" and its Generalization in Higher Dimensions</A>
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<DT><A HREF="http://arxiv.org/abs/1606.07374v2">Multi-Stage Temporal Difference Learning for 2048-like Games</A>
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<DT><A HREF="http://arxiv.org/abs/1408.6315v1">2048 is (PSPACE) Hard, but Sometimes Easy</A>
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<DT><A HREF="https://jonzia.github.io/Catan/">Settlers of Catan bot trained using reinforcement learning</A>
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<DT><A HREF="https://www.aaai.org/ocs/index.php/AIIDE/AIIDE18/paper/viewFile/18091/17217">POMCP with Human Preferencesin Settlers of Catan</A>
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<DT><A HREF="https://izbicki.me/blog/how-to-cheat-at-settlers-of-catan-by-loading-the-dice-and-prove-it-with-p-values.html">The impact of loaded dice in Catan</A>
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<DT><A HREF="https://project.dke.maastrichtuniversity.nl/games/files/bsc/Roelofs_Bsc-paper.pdf">Monte Carlo Tree Search in a Modern Board Game Framework</A>
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<DT><A HREF="http://www.ke.tu-darmstadt.de/lehre/archiv/ss04/oberseminar/folien/Wolf_Michael-Slides.pdf">An Intelligent Artificial Player for the Game of Risk</A>
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<DT><A HREF="https://scholar.rose-hulman.edu/rhumj/vol3/iss2/3">RISKy Business: An In-Depth Look at the Game RISK</A>
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<DT><A HREF="http://www.c4i.gr/xgeorgio/docs/RISK-board-game%20_rev-3.pdf">RISK Board Game ‐ Battle Outcome Analysis</A>
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<DT><A HREF="https://zayenz.se/blog/post/patchwork-modref2019-paper/">State Representation and Polyomino Placement for the Game Patchwork</A>
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<DT><A HREF="http://arxiv.org/abs/2001.04233">State Representation and Polyomino Placement for the Game Patchwork</A>
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<DT><A HREF="https://zayenz.se/papers/Lagerkvist_ModRef_2019_Presentation.pdf">State Representation and Polyomino Placement for the Game Patchwork</A>
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<DT><A HREF="http://arxiv.org/abs/2001.04238">Nmbr9 as a Constraint Programming Challenge</A>
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<DT><A HREF="https://zayenz.se/blog/post/nmbr9-cp2019-abstract/">Nmbr9 as a Constraint Programming Challenge</A>
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<DT><A HREF="https://ieeexplore.ieee.org/document/8490449/">Evolving Agents for the Hanabi 2018 CIG Competition</A>
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<DT><A HREF="http://link.springer.com/10.1007/978-3-319-67468-1_7">Aspects of the Cooperative Card Game Hanabi</A>
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<DT><A HREF="http://link.springer.com/10.1007/978-3-319-71649-7_5">Playing Hanabi Near-Optimally</A>
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<DT><A HREF="http://ieeexplore.ieee.org/document/8080417/">An intentional AI for hanabi</A>
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<DT><A HREF="https://aaai.org/ocs/index.php/WS/AAAIW15/paper/view/10167">Solving Hanabi: Estimating Hands by Opponent's Actions in Cooperative Game with Incomplete Information</A>
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<DT><A HREF="http://fdg2017.org/papers/FDG2017_demo_Hanabi.pdf">A Browser-based Interface for the Exploration and Evaluation of Hanabi AIs</A>
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<DT><A HREF="https://github.com/WuTheFWasThat/hanabi.rs">State of the art Hanabi bots + simulation framework in rust</A>
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<DT><A HREF="https://github.com/rjtobin/HanSim">A strategy simulator for the well-known cooperative card game Hanabi</A>
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<DT><A HREF="https://github.com/Quuxplusone/Hanabi">A framework for writing bots that play Hanabi</A>
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</DL> |