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# boardgame-research [![Contributions Welcome](https://img.shields.io/badge/Contributions-welcome-brightgreen.svg)](https://github.com/captn3m0/boardgame-research/issues/new/choose)
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This is a list of boardgame research. They are primarily related to "solving/playing/learning" games (by various different approaches), or
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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
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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
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games like Chess, Go, Hex, take a look at the [Chess programming wiki ](https://www.chessprogramming.org/Games ) instead. The list also covers some computer-games that fall under similar themes.
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Exported versions are available in the following formats:
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- [Zotero RDF ](boardgame-research.rdf )
- [BibTeX ](boardgame-research.bib )
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Watch the repository to get the latest updates for now.
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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/ ).
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<!-- START doctoc generated TOC please keep comment here to allow auto update -->
<!-- DON'T EDIT THIS SECTION, INSTEAD RE - RUN doctoc TO UPDATE -->
**Table of Contents** *generated with [DocToc](https://github.com/thlorenz/doctoc)*
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- [Azul ](#azul )
- [Blokus ](#blokus )
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- [Carcassonne ](#carcassonne )
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- [Diplomacy ](#diplomacy )
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- [Dixit ](#dixit )
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- [Dominion ](#dominion )
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- [Hanabi ](#hanabi )
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- [Hive ](#hive )
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- [Jenga ](#jenga )
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- [Kingdomino ](#kingdomino )
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- [Lost Cities ](#lost-cities )
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- [Mafia ](#mafia )
- [Magic: the Gathering ](#magic-the-gathering )
- [Modern Art: The card game ](#modern-art-the-card-game )
- [Monopoly ](#monopoly )
- [Monopoly Deal ](#monopoly-deal )
- [Nmbr9 ](#nmbr9 )
- [Pandemic ](#pandemic )
- [Patchwork ](#patchwork )
- [Pentago ](#pentago )
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- [Quixo ](#quixo )
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- [Race for the Galaxy ](#race-for-the-galaxy )
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- [The Resistance: Avalon ](#the-resistance-avalon )
- [Risk ](#risk )
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- [Santorini ](#santorini )
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- [Scotland Yard ](#scotland-yard )
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- [Secret Hitler ](#secret-hitler )
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- [Set ](#set )
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- [Settlers of Catan ](#settlers-of-catan )
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- [Shobu ](#shobu )
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- [Terra Mystica ](#terra-mystica )
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- [Tetris Link ](#tetris-link )
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- [Ticket to Ride ](#ticket-to-ride )
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- [Ultimate Tic-Tac-Toe ](#ultimate-tic-tac-toe )
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- [Uno ](#uno )
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- [Yahtzee ](#yahtzee )
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- [Mobile Games ](#mobile-games )
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- [2048 ](#2048 )
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- [Game Design ](#game-design )
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- [Accessibility ](#accessibility )
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- [Frameworks/Toolkits ](#frameworkstoolkits )
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<!-- END doctoc generated TOC please keep comment here to allow auto update -->
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# Azul
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- [A summary of a dissertation on Azul ](https://old.reddit.com/r/boardgames/comments/hxodaf/update_i_wrote_my_dissertation_on_azul/ ) (unpublished)
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- [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 ) [[GitHub ](https://github.com/Swynfel/ceramic )]
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# Blokus
- [Blokus Game Solver ](https://digitalcommons.calpoly.edu/cpesp/290/ )
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- [FPGA Blokus Duo Solver using a massively parallel architecture ](https://doi.org/10.1109/FPT.2013.6718426 )
- [Blokus Duo game on FPGA ](https://doi.org/10.1109/CADS.2013.6714256 )
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# Carcassonne
- [Playing Carcassonne with Monte Carlo Tree Search ](https://arxiv.org/abs/2009.12974 )
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# Diplomacy
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- [Human-Level Performance in No-Press Diplomacy via Equilibrium Search ](https://arxiv.org/abs/2010.02923 )
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- [Learning to Play No-Press Diplomacy with Best Response Policy Iteration ](https://arxiv.org/abs/2006.04635 )
- [No Press Diplomacy: Modeling Multi-Agent Gameplay ](https://arxiv.org/abs/1909.02128 )
- [Agent Madoff: A Heuristic-Based Negotiation Agent For The Diplomacy Strategy Game ](https://arxiv.org/abs/1902.06996 )
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# Dixit
- [Creative Captioning: An AI Grand Challenge Based on the Dixit Board Game ](https://arxiv.org/abs/2010.00048 )
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- [Dixit: Interactive Visual Storytelling via Term Manipulation ](https://arxiv.org/abs/1903.02230 )
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# Dominion
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There is a [simulator ](https://dominionsimulator.wordpress.com/f-a-q/ ) and the code behind
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[the Dominion server running councilroom.com ](https://github.com/mikemccllstr/dominionstats/ ) is available. councilroom has the [best and worst openings ](http://councilroom.com/openings ), [optimal card ratios ](http://councilroom.com/optimal_card_ratios ), [Card winning stats ](http://councilroom.com/supply_win ) and lots of other empirical research. The [Dominion Strategy Forum ](http://forum.dominionstrategy.com/index.php ) is another good general resource.
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- [Clustering Player Strategies from Variable-Length Game Logs in Dominion ](https://arxiv.org/abs/1811.11273 )
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# Hanabi
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- [Improving Policies via Search in Cooperative Partially Observable Games ](https://arxiv.org/abs/1912.02318 ) (FB) [[code ](https://github.com/facebookresearch/Hanabi_SPARTA )] - Current best result.
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- [Re-determinizing MCTS in Hanabi ](https://ieee-cog.org/2020/papers2019/paper_17.pdf )
- [Hanabi is NP-hard, Even for Cheaters who Look at Their Cards ](https://arxiv.org/abs/1603.01911 )
- [Evolving Agents for the Hanabi 2018 CIG Competition ](https://ieeexplore.ieee.org/abstract/document/8490449 )
- [Aspects of the Cooperative Card Game Hanabi ](https://link.springer.com/chapter/10.1007/978-3-319-67468-1_7 )
- [How to Make the Perfect Fireworks Display: Two Strategies for Hanabi ](https://doi.org/10.4169/math.mag.88.5.323 )
- [Playing Hanabi Near-Optimally ](https://link.springer.com/chapter/10.1007/978-3-319-71649-7_5 )
- [Evaluating and modelling Hanabi-playing agents ](https://doi.org/10.1109/CEC.2017.7969465 )
- [An intentional AI for hanabi ](https://ieeexplore.ieee.org/abstract/document/8080417 )
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- [The Hanabi challenge: A new frontier for AI research ](https://doi.org/10.1016/j.artint.2019.103216 ) [[arXiv ](https://arxiv.org/abs/1902.00506 )]] (DeepMind)
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- [Solving Hanabi: Estimating Hands by Opponent's Actions in Cooperative Game with Incomplete Information ](https://www.aaai.org/ocs/index.php/WS/AAAIW15/paper/view/10167/10193 )
- [A Browser-based Interface for the Exploration and Evaluation of Hanabi AIs ](http://fdg2017.org/papers/FDG2017_demo_Hanabi.pdf )
- [I see what you see: Integrating eye tracking into Hanabi playing agents ](http://www.exag.org/wp-content/uploads/2018/10/AIIDE-18_Upload_112.pdf )
- [The 2018 Hanabi competition ](https://doi.org/10.1109/CIG.2019.8848008 )
- [Diverse Agents for Ad-Hoc Cooperation in Hanabi ](https://doi.org/10.1109/CIG.2019.8847944 ) [[arXiv ](https://arxiv.org/pdf/2004.13710v2.pdf )]
- [State of the art Hanabi bots + simulation framework in rust ](https://github.com/WuTheFWasThat/hanabi.rs )
- [A strategy simulator for the well-known cooperative card game Hanabi ](https://github.com/rjtobin/HanSim )
- [A framework for writing bots that play Hanabi ](https://github.com/Quuxplusone/Hanabi )
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- [Evaluating the Rainbow DQN Agent in Hanabi with Unseen Partners ](https://arxiv.org/abs/2004.13291 )
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- [Operationalizing Intentionality to Play Hanabi with Human Players ](https://doi.org/10.1109/TG.2020.3009359 )
- [Behavioral Evaluation of Hanabi Rainbow DQN Agents and Rule-Based Agents ](https://ojs.aaai.org//index.php/AIIDE/article/view/7404 ) [[pdf ](https://ojs.aaai.org/index.php/AIIDE/article/view/7404/7333 )]
- [Playing mini-Hanabi card game with Q-learning ](http://id.nii.ac.jp/1001/00205046/ )
- [Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi ](https://arxiv.org/abs/2004.13710 )
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- [Hanabi Open Agend Dataset ](https://github.com/aronsar/hoad ) - [[ACM ](https://dl.acm.org/doi/abs/10.5555/3461017.3461244 )]
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# Hive
- [On the complexity of Hive ](https://dspace.library.uu.nl/handle/1874/396955 )
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# Jenga
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- [Maximum genus of the Jenga like configurations ](https://arxiv.org/abs/1708.01503 )
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- [Jidoukan Jenga: Teaching English through remixing games and game rules ](https://www.llpjournal.org/2020/04/13/jidokan-jenga.html )
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# Kingdomino
- [Monte Carlo Methods for the Game Kingdomino ](https://doi.org/10.1109/CIG.2018.8490419 ) [[arXiv ](https://arxiv.org/abs/1807.04458 )]
- [NP-completeness of the game Kingdomino ](https://arxiv.org/abs/1909.02849 )
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# Lost Cities
- [Applying Neural Networks and Genetic Programming to the Game Lost Cities ](http://digital.library.wisc.edu/1793/79080 )
# Mafia
- [A mathematical model of the Mafia game ](https://arxiv.org/abs/1009.1031 )
- [Automatic Long-Term Deception Detection in Group Interaction Videos ](https://arxiv.org/abs/1905.08617 )
- [Human-Side Strategies in the Werewolf Game Against the Stealth Werewolf Strategy ](https://link.springer.com/chapter/10.1007/978-3-319-50935-8_9 )
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- [A Theoretical Study of Mafia Games ](https://arxiv.org/abs/0804.0071 )
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# Magic: the Gathering
- [Magic: the Gathering is as Hard as Arithmetic ](https://arxiv.org/abs/2003.05119 )
- [Magic: The Gathering is Turing Complete ](https://arxiv.org/abs/1904.09828 )
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- [Neural Networks Models for Analyzing Magic: the Gathering Cards ](https://link.springer.com/chapter/10.1007/978-3-030-04179-3_20 ) [[arXiv ](https://arxiv.org/abs/1810.03744 )]
- [The Complexity of Deciding Legality of a Single Step of Magic: the Gathering ](https://livrepository.liverpool.ac.uk/3029568/1/magic.pdf )
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- [Ensemble Determinization in Monte Carlo Tree Search for the Imperfect Information Card Game Magic: The Gathering ](https://doi.org/10.1109/TCIAIG.2012.2204883 )
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- [Magic: The Gathering in Common Lisp ](https://pdfs.semanticscholar.org/5fc8/58802f19504ea950e20e31526dc2269b43d8.pdf ) [[source ](https://github.com/jeffythedragonslayer/maglisp )]
- [Deck Costruction Strategies for Magic: the Gathering ](https://cab.unime.it/journals/index.php/congress/article/viewFile/141/141 )
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- [Deckbuilding in Magic: The Gathering Using a Genetic Algorithm ](http://hdl.handle.net/11250/2462429 )
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- [Mathematical programming and Magic: The Gathering® ](https://commons.lib.niu.edu/handle/10843/19194 )
- [Optimal Card-Collecting Strategies for Magic: The Gathering ](https://doi.org/10.1080/07468342.2000.11974103 )
- [Monte Carlo search applied to card selection in Magic: The Gathering ](https://doi.org/10.1109/CIG.2009.5286501 )
- [Magic: The Gathering Deck Performance Prediction ](http://cs229.stanford.edu/proj2012/HauPlotkinTran-MagicTheGatheringDeckPerformancePrediction.pdf )
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# Modern Art: The card game
- [A constraint programming based solver for Modern Art ](https://github.com/captn3m0/modernart )
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# Monopoly
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- [Learning Monopoly Gameplay: A Hybrid Model-Free Deep Reinforcement Learning and Imitation Learning Approach ](https://arxiv.org/abs/2103.00683 )
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- [Negotiation strategy of agents in the MONOPOLY game ](https://ieeexplore.ieee.org/abstract/document/1013210 )
- [Generating interesting Monopoly boards from open data ](https://ieeexplore.ieee.org/abstract/document/6374168 )
- [Estimating the probability that the game of Monopoly never ends ](https://ieeexplore.ieee.org/abstract/document/5429349 )
- [Learning to play Monopoly:A Reinforcement Learning approach ](https://www.researchgate.net/profile/Anestis_Fachantidis/publication/289403522_Learning_to_play_monopoly_A_Reinforcement_learning_approach/links/59dd1f3e458515f6efef1904/Learning-to-play-monopoly-A-Reinforcement-learning-approach.pdf )
- [Monopoly as a Markov Process ](https://doi.org/10.1080/0025570X.1972.11976187 )
- [Learning to Play Monopoly withMonte Carlo Tree Search ](https://project-archive.inf.ed.ac.uk/ug4/20181042/ug4_proj.pdf )
- [Monopoly Using Reinforcement Learning ](https://ieeexplore.ieee.org/abstract/document/8929523 )
- [A Markovian Exploration of Monopoly ](https://pi4.math.illinois.edu/wp-content/uploads/2014/10/Gartland-Burson-Ferguson-Markovopoly.pdf )
- [What's the best Monopoly strategy ](https://publications.lakeforest.edu/cgi/viewcontent.cgi?referer=&httpsredir=1&article=1277&context=gss )
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# Monopoly Deal
- [Implementation of AI Player on "Monopoly Deal" ](https://doi.org/10.1007/978-3-662-46742-8_11 )
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# Nmbr9
- [Nmbr9 as a Constraint Programming Challenge ](https://zayenz.se/blog/post/nmbr9-cp2019-abstract/ )
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# Pandemic
- [NP-Completeness of Pandemic ](https://www.jstage.jst.go.jp/article/ipsjjip/20/3/20_723/_article )
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# Patchwork
- [State Representation and Polyomino Placement for the Game Patchwork ](https://zayenz.se/blog/post/patchwork-modref2019-paper/ )
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# Pentago
- [On Solving Pentago ](http://www.ke.tu-darmstadt.de/lehre/arbeiten/bachelor/2011/Buescher_Niklas.pdf )
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# Quixo
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- [Quixo Is Solved ](https://arxiv.org/abs/2007.15895 )
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- [QUIXO is EXPTIME-complete ](https://doi.org/10.1016/j.ipl.2020.105995 )
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# Race for the Galaxy
- [SCOUT: A Case-Based Reasoning Agent for Playing Race for the Galaxy ](https://doi.org/10.1007/978-3-319-61030-6_27 )
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# The Resistance: Avalon
- [Finding Friend and Foe in Multi-Agent Games ](https://arxiv.org/abs/1906.02330 )
# Risk
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- [Mini-Risk: Strategies for a Simplified Board Game ](https://doi.org/10.1057/jors.1990.2 )
- [A Multi-Agent System for playing the board game Risk ](https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A831093&dswid=-4740 )
- [Learning the risk board game with classifier systems ](https://doi.org/10.1145/508791.508904 )
- [Markov Chains and the RISK Board Game ](https://doi.org/10.1080/0025570X.1997.11996573 )
- [Markov Chains for the RISK Board Game Revisited ](https://doi.org/10.1080/0025570X.2003.11953165 )
- [RISK Board Game ‐ Battle Outcome Analysis ](http://www.c4i.gr/xgeorgio/docs/RISK-board-game%20_rev-3.pdf )
- [Planning an endgame move set for the game RISK ](https://doi.org/10.1109/TEVC.2005.856211 )
- [RISKy Business: An In-Depth Look at the Game RISK ](https://scholar.rose-hulman.edu/rhumj/vol3/iss2/3/ )
- [An Intelligent Artificial Player for the Game of Risk ](http://www.ke.tu-darmstadt.de/lehre/archiv/ss04/oberseminar/folien/Wolf_Michael-Slides.pdf )
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- [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 ) [[Presentation ](https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-SAS-OCS-ORA-2020/MP-SAS-OCS-ORA-2020-WCM-01P.pdf )]
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# Santorini
- [A Mathematical Analysis of the Game of Santorini ](https://openworks.wooster.edu/independentstudy/8917/ )
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# Scotland Yard
- [The complexity of Scotland Yard ](http://www.illc.uva.nl/Research/Publications/Reports/PP-2006-18.text.pdf )
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# Secret Hitler
- [Competing in a Complex Hidden Role Game with Information Set Monte Carlo Tree Search ](https://arxiv.org/abs/2005.07156 )
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# Set
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Set has a long history of mathematical research, so this list isn't exhaustive.
- [Game, Set, Math ](https://doi.org/10.4169/math.mag.85.2.083 )
- [The Joy of SET ](https://doi.org/10.1080/00029890.2018.1412661 )
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# Settlers of Catan
- [The effectiveness of persuasion in The Settlers of Catan ](https://doi.org/10.1109/CIG.2014.6932861 )
- [Avoiding Revenge Using Optimal Opponent Ranking Strategy in the Board Game Catan ](https://doi.org/10.4018/IJGCMS.2018040103 )
- [Game strategies for The Settlers of Catan ](https://doi.org/10.1109/CIG.2014.6932884 )
- [Monte-Carlo Tree Search in Settlers of Catan ](https://doi.org/10.1007/978-3-642-12993-3_3 )
- [Settlers of Catan bot trained using reinforcement learning (MATLAB). ](https://jonzia.github.io/Catan/ )
- [Trading in a multiplayer board game: Towards an analysis of non-cooperative dialogue ](https://escholarship.org/content/qt9zt506xx/qt9zt506xx.pdf )
- [POMCP with Human Preferencesin Settlers of Catan ](https://www.aaai.org/ocs/index.php/AIIDE/AIIDE18/paper/viewFile/18091/17217 )
- [Reinforcement Learning of Strategies for Settlers of Catan ](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.561.6293&rep=rep1&type=pdf )
- [Deep Reinforcement Learning in Strategic Board GameEnvironments ](https://doi.org/10.1007/978-3-030-14174-5_16 ) [[pdf ](https://hal.archives-ouvertes.fr/hal-02124411/document )]
- [Monte Carlo Tree Search in a Modern Board Game Framework ](https://project.dke.maastrichtuniversity.nl/games/files/bsc/Roelofs_Bsc-paper.pdf )
- [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 )
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- [Playing Catan with Cross-dimensional Neural Network ](https://arxiv.org/abs/2008.07079 )
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- [Strategic Dialogue Management via Deep Reinforcement Learning ](https://arxiv.org/abs/1511.08099 )
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# Shobu
- [Shobu AI Playground ](https://github.com/JayWalker512/Shobu )
- [Shobu randomly played games dataset ](https://www.kaggle.com/bsfoltz/shobu-randomly-played-games-104k )
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# Terra Mystica
- [Using Tabu Search Algorithm for Map Generation in the Terra Mystica Tabletop Game ](https://arxiv.org/abs/2006.02716 )
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# [Tetris Link](https://boardgamegeek.com/boardgame/93185/tetris-link)
- [A New Challenge: Approaching Tetris Link with AI ](https://arxiv.org/abs/2004.00377 )
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# Ticket to Ride
- [Evolving maps and decks for ticket to ride ](https://doi.org/10.1145/3235765.3235813 )
- [Materials for Ticket to Ride Seattle and a framework for making more game boards ](https://github.com/dovinmu/ttr_generator )
- [The Difficulty of Learning Ticket to Ride ](https://www.eecs.tufts.edu/~jsinapov/teaching/comp150_RL/reports/Nguyen_Dinjian_report.pdf )
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- [AI-based Playtesting of Contemporary Board Games ](https://doi.org/10.1145/3102071.3102105 ) [[pdf ](http://game.engineering.nyu.edu/wp-content/uploads/2017/06/ticket-ride-fdg2017-camera-ready.pdf )] [[presentation ](https://www.rtealwitter.com/slides/2020-JMM.pdf )]
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# Ultimate Tic-Tac-Toe
- [At Most 43 Moves, At Least 29: Optimal Strategies and Bounds for Ultimate Tic-Tac-Toe ](https://arxiv.org/abs/2006.02353 )
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# Uno
- [The complexity of UNO ](https://arxiv.org/abs/1003.2851 )
- [UNO Is Hard, Even for a Single Player ](https://doi.org/10.1007/978-3-642-13122-6_15 )
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# Yahtzee
- [Optimal Solitaire Yahtzee Strategies ](http://www.yahtzee.org.uk/optimal_yahtzee_TV.pdf )
- [Nearly Optimal Computer Play in Multi-player Yahtzee ](https://doi.org/10.1007/978-3-642-17928-0_23 )
- [Computer Strategies for Solitaire Yahtzee ](https://doi.org/10.1109/CIG.2007.368089 )
- [An optimal strategy for Yahtzee ](http://www.cs.loyola.edu/~jglenn/research/optimal_yahtzee.pdf )
- [Yahtzee: a Large Stochastic Environment for RL Benchmarks ](https://pdfs.semanticscholar.org/f5c2/e9c9b17f584f060a73036109f697ac819a23.pdf )
- [Modeling expert problem solving in a game of chance: a Yahtzee case study ](https://doi.org/10.1111/1468-0394.00160 )
- [Probabilites In Yahtzee ](https://doi.org/10.5951/MT.75.9.0751 )
- [Optimal Yahtzee performance in multi-player games ](https://www.diva-portal.org/smash/get/diva2:668705/FULLTEXT01.pdf )
- [Defensive Yahtzee ](https://www.diva-portal.org/smash/get/diva2:817838/FULLTEXT01.pdf )
- [Using Deep Q-Learning to Compare Strategy Ladders of Yahtzee ](https://pdfs.semanticscholar.org/6bec/1c34c8ace65adc95d39cb0c0e589ae392678.pdf )
- [How to Maximize Your Score in Solitaire Yahtzee ](http://www-set.win.tue.nl/~wstomv/misc/yahtzee/yahtzee-report-unfinished.pdf )
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# Mobile Games
- [Trainyard is NP-Hard ](https://arxiv.org/abs/1603.00928 )
- [Threes!, Fives, 1024!, and 2048 are Hard ](https://arxiv.org/abs/1505.04274 )
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## 2048
- [Making Change in 2048 ](https://arxiv.org/abs/1804.07396 )
- [Analysis of the Game "2048" and its Generalization in Higher Dimensions ](https://arxiv.org/abs/1804.07393 )
- [Temporal difference learning of N-tuple networks for the game 2048 ](https://ieeexplore.ieee.org/abstract/document/6932907 )
- [Multi-Stage Temporal Difference Learning for 2048-like Games ](https://arxiv.org/abs/1606.07374 )
- [On the Complexity of Slide-and-Merge Games ](https://arxiv.org/abs/1501.03837 )
- [2048 is (PSPACE) Hard, but Sometimes Eas ](https://arxiv.org/abs/1408.6315 )
- [Systematic Selection of N-Tuple Networks for 2048 ](https://doi.org/10.1007/978-3-319-50935-8_8 )
- [Systematic selection of N-tuple networks with consideration of interinfluence for game 2048 ](https://doi.org/10.1109/TAAI.2016.7880154 )
- [2048 Without New Tiles Is Still Hard ](https://drops.dagstuhl.de/opus/volltexte/2016/5885/ )
- [An investigation into 2048 AI strategies ](https://doi.org/10.1109/CIG.2014.6932920 )
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# Game Design
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- [MDA: A Formal Approach to Game Design and Game Research ](https://www.aaai.org/Papers/Workshops/2004/WS-04-04/WS04-04-001.pdf )
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- [Exploring Anonymity in Cooperative Board Games ](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.225.5554&rep=rep1&type=pdf )
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## Accessibility
- [Eighteen Months of Meeple Like Us: An Exploration into the State of Board Game Accessibility ](https://doi.org/10.1007/s40869-018-0056-9 )
- [Meeple Centred Design: A Heuristic Toolkit for Evaluating the Accessibility of Tabletop Games ](https://doi.org/10.1007/s40869-018-0057-8 )
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# Frameworks/Toolkits
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- [RLCard: A Toolkit for Reinforcement Learning in Card Games ](https://arxiv.org/abs/1910.04376 )
- [GTSA: Game Tree Search Algorithms ](https://github.com/AdamStelmaszczyk/gtsa )
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- [Design and Implementation of TAG: A Tabletop Games Framework ](https://arxiv.org/abs/2009.12065 ) [[GitHub ](https://github.com/GAIGResearch/TabletopGames )]
- [TAG: Tabletop Games Framework ](https://github.com/GAIGResearch/TabletopGames )