Generate Markdown file from Zotero Export (#7)
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# HACKING
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The primary source for everything is my Zotero instance. It exports a RDF file, which is then converted to markdown using XSLT.
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xsltproc to-markdown.xsl boardgame-research.rdf
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# boardgame-research [](http://makeapullrequest.com)
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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
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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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An importable RDF version is available as well:
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- [Zotero RDF](boardgame-research.rdf)
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See Import instructions here: https://www.zotero.org/support/kb/importing_standardized_formats
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[Watch the repository](https://docs.github.com/en/github/managing-subscriptions-and-notifications-on-github/setting-up-notifications/configuring-notifications#configuring-your-watch-settings-for-an-individual-repository) to get the latest updates for now (Choose "All Activity").
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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 -->
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<!-- END doctoc -->
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all:
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xsltproc to-markdown.xsl boardgame-research.rdf > /tmp/contents.md
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cat HEADER.md /tmp/contents.md > README.md
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doctoc README.md
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482
README.md
482
README.md
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@ -6,32 +6,37 @@ should be of interest to anyone interested in boardgames and their optimal play.
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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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An importable RDF version is available as well:
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- [Zotero RDF](boardgame-research.rdf)
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- [BibTeX](boardgame-research.bib)
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Watch the repository to get the latest updates for now.
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See Import instructions here: https://www.zotero.org/support/kb/importing_standardized_formats
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[Watch the repository](https://docs.github.com/en/github/managing-subscriptions-and-notifications-on-github/setting-up-notifications/configuring-notifications#configuring-your-watch-settings-for-an-individual-repository) to get the latest updates for now (Choose "All Activity").
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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 -->
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<!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE -->
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**Table of Contents** *generated with [DocToc](https://github.com/thlorenz/doctoc)*
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- [2048](#2048)
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- [Accessibility](#accessibility)
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- [Azul](#azul)
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- [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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- [Frameworks](#frameworks)
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- [Game Design](#game-design)
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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)
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- [Magic: the Gathering](#magic-the-gathering)
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- [Magic: The Gathering](#magic-the-gathering)
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- [Mobile Games](#mobile-games)
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- [Modern Art: The card game](#modern-art-the-card-game)
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- [Monopoly](#monopoly)
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- [Monopoly Deal](#monopoly-deal)
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@ -41,8 +46,8 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
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- [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)
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- [Risk](#risk)
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- [Resistance: Avalon](#resistance-avalon)
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- [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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@ -53,250 +58,259 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
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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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- [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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# 2048
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- [Systematic Selection of N-Tuple Networks for 2048](https://doi.org/10.1007%2F978-3-319-50935-8_8) (bookSection)
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- [Systematic selection of N-tuple networks with consideration of interinfluence for game 2048](https://doi.org/10.1109%2Ftaai.2016.7880154) (conferencePaper)
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- [An investigation into 2048 AI strategies](https://doi.org/10.1109%2Fcig.2014.6932920) (conferencePaper)
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- [Threes!, Fives, 1024!, and 2048 are Hard](http://arxiv.org/abs/1505.04274v1) (journalArticle)
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- [Making Change in 2048](http://arxiv.org/abs/1804.07396v1) (journalArticle)
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- [Analysis of the Game "2048" and its Generalization in Higher Dimensions](http://arxiv.org/abs/1804.07393v2) (journalArticle)
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- [Multi-Stage Temporal Difference Learning for 2048-like Games](http://arxiv.org/abs/1606.07374v2) (journalArticle)
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- [2048 is (PSPACE) Hard, but Sometimes Easy](http://arxiv.org/abs/1408.6315v1) (journalArticle)
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- [Temporal difference learning of N-tuple networks for the game 2048](http://ieeexplore.ieee.org/document/6932907/) (conferencePaper)
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- [On the Complexity of Slide-and-Merge Games](http://arxiv.org/abs/1501.03837) (journalArticle)
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- [2048 Without New Tiles Is Still Hard](http://drops.dagstuhl.de/opus/volltexte/2016/5885/) (journalArticle)
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# Accessibility
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- [Meeple Centred Design: A Heuristic Toolkit for Evaluating the Accessibility of Tabletop Games](http://link.springer.com/10.1007/s40869-018-0057-8) (journalArticle)
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- [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)
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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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- [A summary of a dissertation on Azul](https://old.reddit.com/r/boardgames/comments/hxodaf/update_i_wrote_my_dissertation_on_azul/) (report)
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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) (conferencePaper)
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- [Ceramic: A research environment based on the multi-player strategic board game Azul](https://github.com/Swynfel/ceramic) (computerProgram)
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# Blokus
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- [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)
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- [Blokus Duo game on FPGA](https://doi.org/10.1109/CADS.2013.6714256)
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- [Blokus Game Solver](https://digitalcommons.calpoly.edu/cpesp/290/) (report)
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- [FPGA Blokus Duo Solver using a massively parallel architecture](http://ieeexplore.ieee.org/document/6718426/) (conferencePaper)
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- [Blokus Duo game on FPGA](http://ieeexplore.ieee.org/document/6714256/) (conferencePaper)
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# Carcassonne
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- [Playing Carcassonne with Monte Carlo Tree Search](https://arxiv.org/abs/2009.12974)
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- [Playing Carcassonne with Monte Carlo Tree Search](http://arxiv.org/abs/2009.12974) (journalArticle)
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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)
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- [No Press Diplomacy: Modeling Multi-Agent Gameplay ](https://arxiv.org/abs/1909.02128)
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- [Agent Madoff: A Heuristic-Based Negotiation Agent For The Diplomacy Strategy Game ](https://arxiv.org/abs/1902.06996)
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- [Learning to Play No-Press Diplomacy with Best Response Policy Iteration](http://arxiv.org/abs/2006.04635v2) (journalArticle)
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- [No Press Diplomacy: Modeling Multi-Agent Gameplay](http://arxiv.org/abs/1909.02128v2) (journalArticle)
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- [Agent Madoff: A Heuristic-Based Negotiation Agent For The Diplomacy Strategy Game](http://arxiv.org/abs/1902.06996v1) (journalArticle)
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- [Monte Carlo Tree Search for the Game of Diplomacy](https://dl.acm.org/doi/10.1145/3411408.3411413) (conferencePaper)
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- [Human-Level Performance in No-Press Diplomacy via Equilibrium Search](http://arxiv.org/abs/2010.02923) (journalArticle)
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# Dixit
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- [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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- [Creative Captioning: An AI Grand Challenge Based on the Dixit Board Game](http://arxiv.org/abs/2010.00048) (journalArticle)
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- [Dixit: Interactive Visual Storytelling via Term Manipulation](http://arxiv.org/abs/1903.02230) (journalArticle)
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# Dominion
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- [Dominion Simulator](https://dominionsimulator.wordpress.com/f-a-q/) (computerProgram)
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- [Dominion Simulator Source Code](https://github.com/mikemccllstr/dominionstats/) (computerProgram)
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- [Best and worst openings in Dominion](http://councilroom.com/openings) (blogPost)
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- [Optimal Card Ratios in Dominion](http://councilroom.com/optimal_card_ratios) (blogPost)
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- [Card Winning Stats on Dominion Server](http://councilroom.com/supply_win) (blogPost)
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- [Dominion Strategy Forum](http://forum.dominionstrategy.com/index.php) (forumPost)
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- [Clustering Player Strategies from Variable-Length Game Logs in Dominion](http://arxiv.org/abs/1811.11273) (journalArticle)
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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)
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- [Hanabi is NP-hard, Even for Cheaters who Look at Their Cards](https://arxiv.org/abs/1603.01911)
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- [Evolving Agents for the Hanabi 2018 CIG Competition](https://ieeexplore.ieee.org/abstract/document/8490449)
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- [Aspects of the Cooperative Card Game Hanabi](https://link.springer.com/chapter/10.1007/978-3-319-67468-1_7)
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- [How to Make the Perfect Fireworks Display: Two Strategies for Hanabi](https://doi.org/10.4169/math.mag.88.5.323)
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- [Playing Hanabi Near-Optimally](https://link.springer.com/chapter/10.1007/978-3-319-71649-7_5)
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- [Evaluating and modelling Hanabi-playing agents](https://doi.org/10.1109/CEC.2017.7969465)
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- [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)
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- [A Browser-based Interface for the Exploration and Evaluation of Hanabi AIs](http://fdg2017.org/papers/FDG2017_demo_Hanabi.pdf)
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- [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)
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- [The 2018 Hanabi competition](https://doi.org/10.1109/CIG.2019.8848008)
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- [Diverse Agents for Ad-Hoc Cooperation in Hanabi](https://doi.org/10.1109/CIG.2019.8847944) [[arXiv](https://arxiv.org/pdf/2004.13710v2.pdf)]
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- [State of the art Hanabi bots + simulation framework in rust](https://github.com/WuTheFWasThat/hanabi.rs)
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- [A strategy simulator for the well-known cooperative card game Hanabi](https://github.com/rjtobin/HanSim)
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- [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)
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- [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)]
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- [Playing mini-Hanabi card game with Q-learning](http://id.nii.ac.jp/1001/00205046/)
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- [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
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- [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
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- [Monte Carlo Methods for the Game Kingdomino](https://doi.org/10.1109/CIG.2018.8490419) [[arXiv](https://arxiv.org/abs/1807.04458)]
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- [NP-completeness of the game Kingdomino](https://arxiv.org/abs/1909.02849)
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# Lost Cities
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- [Applying Neural Networks and Genetic Programming to the Game Lost Cities](http://digital.library.wisc.edu/1793/79080)
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# Mafia
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- [A mathematical model of the Mafia game](https://arxiv.org/abs/1009.1031)
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- [Automatic Long-Term Deception Detection in Group Interaction Videos](https://arxiv.org/abs/1905.08617)
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- [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
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- [Magic: the Gathering is as Hard as Arithmetic](https://arxiv.org/abs/2003.05119)
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- [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)]
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- [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)]
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- [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)
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- [Optimal Card-Collecting Strategies for Magic: The Gathering](https://doi.org/10.1080/07468342.2000.11974103)
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- [Monte Carlo search applied to card selection in Magic: The Gathering](https://doi.org/10.1109/CIG.2009.5286501)
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- [Magic: The Gathering Deck Performance Prediction](http://cs229.stanford.edu/proj2012/HauPlotkinTran-MagicTheGatheringDeckPerformancePrediction.pdf)
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# Modern Art: The card game
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- [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)
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- [Generating interesting Monopoly boards from open data](https://ieeexplore.ieee.org/abstract/document/6374168)
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- [Estimating the probability that the game of Monopoly never ends](https://ieeexplore.ieee.org/abstract/document/5429349)
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- [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)
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- [Monopoly as a Markov Process](https://doi.org/10.1080/0025570X.1972.11976187)
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- [Learning to Play Monopoly withMonte Carlo Tree Search](https://project-archive.inf.ed.ac.uk/ug4/20181042/ug4_proj.pdf)
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- [Monopoly Using Reinforcement Learning ](https://ieeexplore.ieee.org/abstract/document/8929523)
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- [A Markovian Exploration of Monopoly](https://pi4.math.illinois.edu/wp-content/uploads/2014/10/Gartland-Burson-Ferguson-Markovopoly.pdf)
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- [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
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- [Implementation of AI Player on "Monopoly Deal"](https://doi.org/10.1007/978-3-662-46742-8_11)
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# Nmbr9
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- [Nmbr9 as a Constraint Programming Challenge](https://zayenz.se/blog/post/nmbr9-cp2019-abstract/)
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# Pandemic
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- [NP-Completeness of Pandemic](https://www.jstage.jst.go.jp/article/ipsjjip/20/3/20_723/_article)
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# Patchwork
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||||
- [State Representation and Polyomino Placement for the Game Patchwork](https://zayenz.se/blog/post/patchwork-modref2019-paper/)
|
||||
|
||||
# Pentago
|
||||
- [On Solving Pentago](http://www.ke.tu-darmstadt.de/lehre/arbeiten/bachelor/2011/Buescher_Niklas.pdf)
|
||||
|
||||
# Quixo
|
||||
- [Quixo Is Solved](https://arxiv.org/abs/2007.15895)
|
||||
- [QUIXO is EXPTIME-complete](https://doi.org/10.1016/j.ipl.2020.105995)
|
||||
|
||||
# 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)
|
||||
|
||||
# The Resistance: Avalon
|
||||
- [Finding Friend and Foe in Multi-Agent Games](https://arxiv.org/abs/1906.02330)
|
||||
|
||||
# Risk
|
||||
|
||||
- [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)
|
||||
- [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)]
|
||||
|
||||
# Santorini
|
||||
- [A Mathematical Analysis of the Game of Santorini](https://openworks.wooster.edu/independentstudy/8917/)
|
||||
|
||||
# Scotland Yard
|
||||
- [The complexity of Scotland Yard](http://www.illc.uva.nl/Research/Publications/Reports/PP-2006-18.text.pdf)
|
||||
|
||||
# Secret Hitler
|
||||
- [Competing in a Complex Hidden Role Game with Information Set Monte Carlo Tree Search](https://arxiv.org/abs/2005.07156)
|
||||
|
||||
# Set
|
||||
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)
|
||||
|
||||
# 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)
|
||||
- [Playing Catan with Cross-dimensional Neural Network](https://arxiv.org/abs/2008.07079)
|
||||
- [Strategic Dialogue Management via Deep Reinforcement Learning](https://arxiv.org/abs/1511.08099)
|
||||
|
||||
# Shobu
|
||||
- [Shobu AI Playground](https://github.com/JayWalker512/Shobu)
|
||||
- [Shobu randomly played games dataset](https://www.kaggle.com/bsfoltz/shobu-randomly-played-games-104k)
|
||||
|
||||
# Terra Mystica
|
||||
- [Using Tabu Search Algorithm for Map Generation in the Terra Mystica Tabletop Game](https://arxiv.org/abs/2006.02716)
|
||||
|
||||
# [Tetris Link](https://boardgamegeek.com/boardgame/93185/tetris-link)
|
||||
- [A New Challenge: Approaching Tetris Link with AI](https://arxiv.org/abs/2004.00377)
|
||||
|
||||
# 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)
|
||||
- [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)]
|
||||
|
||||
# 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)
|
||||
|
||||
# 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)
|
||||
|
||||
# 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)
|
||||
|
||||
# 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)
|
||||
|
||||
## 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)
|
||||
# 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 Design
|
||||
- [MDA: A Formal Approach to Game Design and Game Research ](https://www.aaai.org/Papers/Workshops/2004/WS-04-04/WS04-04-001.pdf)
|
||||
- [Exploring Anonymity in Cooperative Board Games](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.225.5554&rep=rep1&type=pdf)
|
||||
- [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)
|
||||
|
||||
## 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)
|
||||
# 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)
|
||||
|
||||
# Frameworks/Toolkits
|
||||
- [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)
|
||||
- [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)
|
||||
# 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)
|
||||
|
||||
# Kingdomino
|
||||
- [Monte Carlo Methods for the Game Kingdomino](https://doi.org/10.1109%2Fcig.2018.8490419) (conferencePaper)
|
||||
- [Monte Carlo Methods for the Game Kingdomino](http://arxiv.org/abs/1807.04458v2) (journalArticle)
|
||||
- [NP-completeness of the game Kingdomino](http://arxiv.org/abs/1909.02849v3) (journalArticle)
|
||||
|
||||
# Lost Cities
|
||||
- [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)
|
||||
|
||||
# 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)
|
||||
- [Magic: The Gathering Deck Performance Prediction](http://cs229.stanford.edu/proj2012/HauPlotkinTran-MagicTheGatheringDeckPerformancePrediction.pdf) (report)
|
||||
|
||||
# Mobile Games
|
||||
- [Trainyard is NP-Hard](http://arxiv.org/abs/1603.00928v1) (journalArticle)
|
||||
- [Threes!, Fives, 1024!, and 2048 are Hard](http://arxiv.org/abs/1505.04274v1) (journalArticle)
|
||||
|
||||
# Modern Art: The card game
|
||||
- [A constraint programming based solver for Modern Art](https://github.com/captn3m0/modernart) (computerProgram)
|
||||
|
||||
# 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)
|
||||
|
||||
# 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)
|
||||
|
||||
# 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)
|
||||
|
||||
# Pentago
|
||||
- [On Solving Pentago](http://www.ke.tu-darmstadt.de/lehre/arbeiten/bachelor/2011/Buescher_Niklas.pdf) (thesis)
|
||||
|
||||
# Quixo
|
||||
- [QUIXO is EXPTIME-complete](https://doi.org/10.1016%2Fj.ipl.2020.105995) (journalArticle)
|
||||
- [Quixo Is Solved](http://arxiv.org/abs/2007.15895) (journalArticle)
|
||||
|
||||
# Race for the Galaxy
|
||||
- [SCOUT: A Case-Based Reasoning Agent for Playing Race for the Galaxy](https://doi.org/10.1007%2F978-3-319-61030-6_27) (bookSection)
|
||||
|
||||
# Resistance: Avalon
|
||||
- [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)
|
||||
- [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)
|
||||
|
||||
# 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)
|
||||
|
||||
# Scotland Yard
|
||||
- [The complexity of Scotland Yard](https://eprints.illc.uva.nl/id/eprint/193/1/PP-2006-18.text.pdf) (report)
|
||||
|
||||
# Secret Hitler
|
||||
- [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)
|
||||
|
||||
# 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)
|
||||
- [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)
|
||||
- [Strategic Dialogue Management via Deep Reinforcement Learning](http://arxiv.org/abs/1511.08099) (journalArticle)
|
||||
|
||||
# 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)
|
||||
|
||||
# Terra Mystica
|
||||
- [Using Tabu Search Algorithm for Map Generation in the Terra Mystica Tabletop Game](https://doi.org/10.1145%2F3396474.3396492) (conferencePaper)
|
||||
|
||||
# Tetris Link
|
||||
- [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)
|
||||
|
||||
# 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)
|
||||
|
||||
# UNO
|
||||
- [UNO Is Hard, Even for a Single Player](https://doi.org/10.1007%2F978-3-642-13122-6_15) (bookSection)
|
||||
- [The complexity of UNO](http://arxiv.org/abs/1003.2851v3) (journalArticle)
|
||||
|
||||
# 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)
|
||||
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|
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Loading…
Reference in New Issue