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
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
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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- [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)
- [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)
- [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)
- [Algorithm Modeling for Hardware Implementation of a Blokus Duo Player](http://artemis.library.tuc.gr/DT2014-0060/DT2014-0060.pdf) (thesis)
- [Artificial Intelligence for Blokus Classic using Heuristics, FloodFill, and Greedy Algorithm](https://ejournal-medan.uph.edu/index.php/ISD/article/view/433) (journalArticle)
- [FPGA implementation of Blokus Duo player using hardware/software co-design](http://ieeexplore.ieee.org/document/7082825/) (conferencePaper)
- [Exploring Combinatorics and Graph Theory with Simple Blokus](https://www.tandfonline.com/doi/full/10.1080/07468342.2022.2100147) (journalArticle)
- [An FPGA-based specific processor for Blokus Duo](http://ieeexplore.ieee.org/document/6718428/) (conferencePaper)
- [Blokus Game Solver](https://digitalcommons.calpoly.edu/cpesp/290/) (report)
- [Highly scalable, shared-memory, Monte-Carlo tree search based Blokus Duo Solver on FPGA](http://ieeexplore.ieee.org/document/7082823/) (conferencePaper)
- [Implementation of a highly scalable blokus duo solver on FPGA](http://ieeexplore.ieee.org/document/6718423/) (conferencePaper)
- [An implementation of Blokus Duo player on FPGA](http://ieeexplore.ieee.org/document/6718429/) (conferencePaper)
- [The Liquid Metal Blokus Duo Design](http://ieeexplore.ieee.org/document/6718425/) (conferencePaper)
- [Artificial intelligence of Blokus Duo on FPGA using Cyber Work Bench](http://ieeexplore.ieee.org/document/6718427/) (conferencePaper)
- [Hardware/software co-design architecture for Blokus Duo solver](http://ieeexplore.ieee.org/document/7082820/) (conferencePaper)
- [From C to Blokus Duo with LegUp high-level synthesis](http://ieeexplore.ieee.org/document/6718424/) (conferencePaper)
- [Blokus Duo engine on a Zynq](http://ieeexplore.ieee.org/document/7082824/) (conferencePaper)
- [Optimize MinMax algorithm to solve Blokus Duo game by HDL](http://ieeexplore.ieee.org/document/7082821/) (conferencePaper)
- [A Case Study of FPGA Blokus Duo Solver by System-Level Design](https://dl.acm.org/doi/10.1145/2693714.2693725) (journalArticle)
- [On the Evolution of the MCTS Upper Confidence Bounds for Trees by Means of Evolutionary Algorithms in the Game of Carcassonne](http://arxiv.org/abs/2112.09697) (journalArticle)
- [Evolving the MCTS Upper Confidence Bounds for Trees Using a Semantic-inspired Evolutionary Algorithm in the Game of Carcassonne](http://arxiv.org/abs/2208.13589) (preprint)
- [Game Balancing in Dominion: An Approach to Identifying Problematic Game Elements](http://cs.gettysburg.edu/~tneller/games/aiagd/papers/EAAI-00039-FordC.pdf) (journalArticle)
- [Player of Games](http://arxiv.org/abs/2112.03178) (journalArticle)
- [A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play](https://www.science.org/doi/10.1126/science.aar6404) (journalArticle)
- [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)
- [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)
- [K-level Reasoning for Zero-Shot Coordination in Hanabi](https://papers.neurips.cc/paper/2021/hash/4547dff5fd7604f18c8ee32cf3da41d7-Abstract.html) (conferencePaper)
- [The Hanabi challenge: From Artificial Teams to Mixed Human-Machine Teams](http://oru.diva-portal.org/smash/record.jsf?pid=diva2%3A1691114&dswid=-1981) (thesis)
- [A Graphical User Interface For The Hanabi Challenge Benchmark](http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-94615) (thesis)
- [Analysis of Symmetry and Conventions in Off-Belief Learning (OBL) in Hanabi](https://fanpu.io/blog/2022/symmetry-and-conventions-in-obl-hanabi/) (blogPost)
- [Using intuitive behavior models to adapt to and work with human teammates in Hanabi](http://reports-archive.adm.cs.cmu.edu/anon/anon/usr0/ftp/usr/ftp/2022/abstracts/22-119.html) (thesis)
- [Multiplayer Tension In the Wild: A Hearthstone Case](https://research.tilburguniversity.edu/en/publications/multiplayer-tension-in-the-wild-a-hearthstone-case) (conferencePaper)
- [Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms](http://arxiv.org/abs/1808.04794) (preprint)
- [Analysis of gameplay strategies in hearthstone: a data science approach](http://archives.njit.edu/vol01/etd/2020s/2020/njit-etd2020-006/njit-etd2020-006.pdf) (thesis)
- [Decision-Making in Hearthstone Based on Evolutionary Algorithm](https://www.cs.tsukuba.ac.jp/~hasebe/downloads/icaart2023_sakurai.pdf) (journalArticle)
- [I am a legend: Hacking hearthstone using statistical learning methods](http://ieeexplore.ieee.org/document/7860416/) (conferencePaper)
- [The Many AI Challenges of Hearthstone](http://link.springer.com/10.1007/s13218-019-00615-z) (journalArticle)
- [Evolutionary deckbuilding in hearthstone](http://ieeexplore.ieee.org/document/7860426/) (conferencePaper)
- [Evolving the Hearthstone Meta](http://arxiv.org/abs/1907.01623) (preprint)
- [Optimizing Hearthstone agents using an evolutionary algorithm](https://linkinghub.elsevier.com/retrieve/pii/S0950705119304356) (journalArticle)
- [Exploring the hearthstone deck space](https://dl.acm.org/doi/10.1145/3235765.3235791) (conferencePaper)
- [Deep Instance Segmentation and Visual Servoing to Play Jenga with a Cost-Effective Robotic System](https://www.mdpi.com/1424-8220/23/2/752) (journalArticle)
- [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)
- [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)
- [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)
- [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)
- [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)
- [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)
- [An Application of Machine Learning to the Board Game Pentago](http://cs229.stanford.edu/proj2012/HeonOetting-AnAppliactionOfMachineLearningToTheBoardGamePentago.pdf) (report)
- [Learning finite functions by neural networks : Evaluation of Pentago positions by convolutional neural networks](https://repository.kulib.kyoto-u.ac.jp/dspace/handle/2433/251730) (report)
# Puerto Rico
- [Artificial Intelligence Techniques for the Puerto Rico Strategy Game](http://link.springer.com/10.1007/978-3-319-59394-4_8) (bookSection)
- [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)
- [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)
- [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)
- [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)
- [Reinforcement Learning of Strategies for Settlers of Catan](https://www.researchgate.net/publication/228728063_Reinforcement_learning_of_strategies_for_Settlers_of_Catan) (conferencePaper)
- [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)
- [Using Ant Colony Optimisation for map generation and improving game balance in the Terra Mystica and Settlers of Catan board games](https://dl.acm.org/doi/10.1145/3402942.3409778) (conferencePaper)
- [Using Ant Colony Optimisation for map generation and improving game balance in the Terra Mystica and Settlers of Catan board games](https://dl.acm.org/doi/10.1145/3402942.3409778) (conferencePaper)
- [Map Generation and Balance in the Terra Mystica Board Game Using Particle Swarm and Local Search](http://link.springer.com/10.1007/978-3-030-53956-6_15) (bookSection)
- [Using Ant Colony Optimisation for map generation and improving game balance in the Terra Mystica and Settlers of Catan board games](https://dl.acm.org/doi/10.1145/3402942.3409778) (conferencePaper)
- [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)
