conferencePaper 2014 IEEE Conference on Computational Intelligence and Games DOI 10.1109/cig.2014.6932861 IEEE Guhe Markus Lascarides Alex The effectiveness of persuasion in The Settlers of Catan August 2014 https://doi.org/10.1109%2Fcig.2014.6932861 attachment Submitted Version https://www.pure.ed.ac.uk/ws/files/19353900/CIG2014.pdf 2020-07-20 18:34:57 1 application/pdf journalArticle 10 International Journal of Gaming and Computer-Mediated Simulations DOI 10.4018/ijgcms.2018040103 2 Boda Márton Attila Avoiding Revenge Using Optimal Opponent Ranking Strategy in the Board Game Catan April 2018 https://doi.org/10.4018%2Fijgcms.2018040103 Publisher: IGI Global 47–70 attachment Full Text https://sci-hub.se/downloads/2020-05-28/3d/boda2018.pdf#view=FitH 2020-07-20 18:22:11 1 application/pdf conferencePaper 2014 IEEE Conference on Computational Intelligence and Games DOI 10.1109/cig.2014.6932884 IEEE Guhe Markus Lascarides Alex Game strategies for The Settlers of Catan August 2014 https://doi.org/10.1109%2Fcig.2014.6932884 attachment Submitted Version https://www.pure.ed.ac.uk/ws/files/19351482/CIG2014_GS.pdf 2020-07-20 18:24:09 1 application/pdf bookSection Lecture Notes in Computer Science Springer Berlin Heidelberg Szita István Chaslot Guillaume Spronck Pieter Monte-Carlo Tree Search in Settlers of Catan 2010 https://doi.org/10.1007%2F978-3-642-12993-3_3 DOI: 10.1007/978-3-642-12993-3_3 21–32 attachment Full Text https://zero.sci-hub.se/5140/3f6b582d932254ee1b7d29e6e9683934/szita2010.pdf#view=FitH 2020-07-20 18:29:58 1 application/pdf bookSection Multi-Agent Systems Springer International Publishing Xenou Konstantia Chalkiadakis Georgios Afantenos Stergos Deep Reinforcement Learning in Strategic Board Game Environments 2019 https://doi.org/10.1007%2F978-3-030-14174-5_16 DOI: 10.1007/978-3-030-14174-5_16 233–248 attachment Accepted Version https://oatao.univ-toulouse.fr/22647/1/xenou_22647.pdf 2020-07-20 18:10:35 1 application/pdf journalArticle 41 Journal of the Operational Research Society DOI 10.1057/jors.1990.2 1 Maliphant Sarah A. Smith David K. Mini-Risk: Strategies for a Simplified Board Game January 1990 https://doi.org/10.1057%2Fjors.1990.2 Publisher: Informa UK Limited 9–16 attachment Full Text https://zero.sci-hub.se/4681/0e142dbe029d345411eb5019cea0b10a/maliphant1990.pdf#view=FitH 2020-07-20 18:28:37 1 application/pdf conferencePaper Proceedings of the 2002 ACM symposium on Applied computing - SAC \textquotesingle02 DOI 10.1145/508791.508904 ACM Press Neves Atila Brasāo Osvaldo Rosa Agostinho Learning the risk board game with classifier systems 2002 https://doi.org/10.1145%2F508791.508904 attachment Full Text https://dacemirror.sci-hub.se/proceedings-article/f9ce3c906d4e89b8aa3b90f15f0dfe20/neves2002.pdf#view=FitH 2020-07-20 18:26:40 1 application/pdf journalArticle 70 Mathematics Magazine DOI 10.1080/0025570x.1997.11996573 5 Tan Bariş Markov Chains and the RISK Board Game December 1997 https://doi.org/10.1080%2F0025570x.1997.11996573 Publisher: Informa UK Limited 349–357 attachment Full Text https://twin.sci-hub.se/6853/3bdc3204e08f60618dca66f19b9cd1fc/markov-chains-and-the-risk-board-game-1997.pdf#view=FitH 2020-07-20 18:28:15 1 application/pdf journalArticle 76 Mathematics Magazine DOI 10.1080/0025570x.2003.11953165 2 Osborne Jason A. Markov Chains for the RISK Board Game Revisited April 2003 https://doi.org/10.1080%2F0025570x.2003.11953165 Publisher: Informa UK Limited 129–135 attachment Full Text https://twin.sci-hub.se/6908/ad9e3c21b4a5edae31079e43ad12c8ce/osborne2003.pdf#view=FitH 2020-07-20 18:28:23 1 application/pdf journalArticle 9 IEEE Trans. Evol. Computat. DOI 10.1109/tevc.2005.856211 6 Vaccaro J. M. Guest C. C. Planning an Endgame Move Set for the Game RISK: A Comparison of Search Algorithms December 2005 https://doi.org/10.1109%2Ftevc.2005.856211 Publisher: Institute of Electrical and Electronics Engineers (IEEE) 641–652 attachment Full Text https://moscow.sci-hub.se/1819/0e61163e1173d174a5261879afc2c42d/vaccaro2005.pdf#view=FitH 2020-07-20 18:31:44 1 application/pdf conferencePaper 2018 IEEE Conference on Computational Intelligence and Games (CIG) DOI 10.1109/cig.2018.8490419 IEEE Gedda Magnus Lagerkvist Mikael Z. Butler Martin Monte Carlo Methods for the Game Kingdomino August 2018 https://doi.org/10.1109%2Fcig.2018.8490419 attachment Submitted Version https://arxiv.org/pdf/1807.04458 2020-07-20 18:29:37 1 application/pdf journalArticle 88 Mathematics Magazine DOI 10.4169/math.mag.88.5.323 5 Cox Christopher Silva Jessica De Deorsey Philip Kenter Franklin H. J. Retter Troy Tobin Josh How to Make the Perfect Fireworks Display: Two Strategies forHanabi December 2015 https://doi.org/10.4169%2Fmath.mag.88.5.323 Publisher: Informa UK Limited 323–336 attachment Full Text https://moscow.sci-hub.se/5019/aae1c968ecb4576818556c669a20e535/christophercox2015.pdf#view=FitH 2020-07-20 18:26:02 1 application/pdf conferencePaper 2017 IEEE Congress on Evolutionary Computation (CEC) DOI 10.1109/cec.2017.7969465 IEEE Walton-Rivers Joseph Williams Piers R. Bartle Richard Perez-Liebana Diego Lucas Simon M. Evaluating and modelling Hanabi-playing agents June 2017 https://doi.org/10.1109%2Fcec.2017.7969465 attachment Accepted Version https://repository.essex.ac.uk/20341/1/1704.07069v1.pdf 2020-07-20 18:16:01 1 application/pdf journalArticle 280 Artificial Intelligence DOI 10.1016/j.artint.2019.103216 Bard Nolan Foerster Jakob N. Chandar Sarath Burch Neil Lanctot Marc Song H. Francis Parisotto Emilio Dumoulin Vincent Moitra Subhodeep Hughes Edward Dunning Iain Mourad Shibl Larochelle Hugo Bellemare Marc G. Bowling Michael The Hanabi challenge: A new frontier for AI research March 2020 https://doi.org/10.1016%2Fj.artint.2019.103216 Publisher: Elsevier BV 103216 attachment Full Text https://pdf.sciencedirectassets.com/271585/1-s2.0-S0004370219X00120/1-s2.0-S0004370219300116/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEHoaCXVzLWVhc3QtMSJGMEQCIH1Rh0U0FaCQrRRyUSiT86nBmiJQoMaX1VTfIk3CSCBuAiBVtUw9mFSXO4P2IR2s2Cqs5kI4CwPLTkYQLjfgja598Sq0AwgzEAMaDDA1OTAwMzU0Njg2NSIMyb025En2S0rT1ZZeKpEDm01bDtAwa5C41YjSxTlisMeVuPdMt%2Fpp3ln9ZE3BEsM0TrGG0EvzEx3c85DHMJoG3oK32WnIGWwPieFnqEZGQJ5kP%2BEtnPQMUzxY7WXMipZQ%2Fmkao3oL%2Fu%2BbKsSHJ%2BAolhMh0G%2F1YvpVoepC6rGy8rku6DXcS0XWgvUzcoLJlcPRRsF5pGQp6xFOR1RW2pR6oq9UoJWEImFDw6X2g2MgPaFMRXMo71FNVX7Zix2oWGhaDRS0hMEerYFvmj1Lv0rmQbAo2h0lvvTZkGWrcauFFEjILxJFadqwcK4Xfe%2BfFDR2H61VZ7B792qzjSC8vCsAToK8BSVepuCnVpDM04cKPnrsiqtR2WMuCMYlS2w%2BLRCk03EjXQvU8ZR8J63MmEPpbJ7pS6JnI%2B0nCcCYtvb7yqcWTzmHjPN6ssNUpPX1ajjjlZyaFm5ntqyCxL0tYar6ra3TkmF2Lbesk%2F1wLTuqnLhsIEmzB01wzw%2BEd%2BxW7PZnDHKM%2FeBPaeg1Z2VaL%2BuwDGWMhBnk8O2sUafc2qT%2BRzMwuLHX%2BAU67AH1wVDVhIqhENwF1N9Pv%2BLhicrQXiHJ79HC1JGf9ulqBM9sLRnFjdyxRYUm6O%2F9RPSV6OTVARQGNQpBBqJUN5%2BCfvHvl%2BVgfEa3fTERLxkX1QBTsTGKAzKee1BjQylRYNnTLhUm0CV56l3jCBMG9LwpodFZgoMHURoqbkWDV9HkAS9W6LnIvcy4L7e76c3qrpB%2B1XoEzqX%2BpQ3S4lRCVR5NcTLnUmxww4%2Br8nzca5CibEM9l033knReVVZnjt2JZVzZVW6zXpO60OH%2F6W2N1qYvsY3oOLT7kX5w7GNCi4qrNANQLGrPXDDqLOuI2A%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20200720T183502Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTY6ZAVFJHH%2F20200720%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=15beeda55689b6fd5e9d2d05b5ac322d5b98fdc5d4f70683cb530821a98eef3c&hash=9fe7e987a2d4bc206328d68796fc691838e0a0ff5373120293f9b5d82fefded1&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0004370219300116&tid=spdf-54a6ffb6-7f42-4d75-84e1-5f15f936d894&sid=76772d953af62548ae38ab517857dea18c60gxrqb&type=client 2020-07-20 18:35:12 1 application/pdf conferencePaper 2019 IEEE Conference on Games (CoG) DOI 10.1109/cig.2019.8848008 IEEE Walton-Rivers Joseph Williams Piers R. Bartle Richard The 2018 Hanabi competition August 2019 https://doi.org/10.1109%2Fcig.2019.8848008 attachment Accepted Version https://repository.essex.ac.uk/26898/2/hanabi.pdf 2020-07-20 18:34:35 1 application/pdf conferencePaper 2019 IEEE Conference on Games (CoG) DOI 10.1109/cig.2019.8847944 IEEE Canaan Rodrigo Togelius Julian Nealen Andy Menzel Stefan Diverse Agents for Ad-Hoc Cooperation in Hanabi August 2019 https://doi.org/10.1109%2Fcig.2019.8847944 attachment Submitted Version https://arxiv.org/pdf/1907.03840 2020-07-20 18:11:10 1 application/pdf journalArticle 45 Mathematics Magazine DOI 10.1080/0025570x.1972.11976187 1 Ash Robert B. Bishop Richard L. Monopoly as a Markov Process January 1972 https://doi.org/10.1080%2F0025570x.1972.11976187 Publisher: Informa UK Limited 26–29 attachment Submitted Version https://www.math.uiuc.edu/%7Ebishop/monopoly.pdf 2020-07-20 18:37:15 1 application/pdf journalArticle 4 IEEE Trans. Comput. Intell. AI Games DOI 10.1109/tciaig.2012.2204883 4 Cowling Peter I. Ward Colin D. Powley Edward J. Ensemble Determinization in Monte Carlo Tree Search for the Imperfect Information Card Game Magic: The Gathering December 2012 https://doi.org/10.1109%2Ftciaig.2012.2204883 Publisher: Institute of Electrical and Electronics Engineers (IEEE) 241–257 attachment Accepted Version https://eprints.whiterose.ac.uk/75050/1/EnsDetMagic.pdf 2020-07-20 18:14:45 1 application/pdf journalArticle 31 The College Mathematics Journal DOI 10.1080/07468342.2000.11974103 1 Bosch Robert A. Optimal Card-Collecting Strategies for Magic: The Gathering January 2000 https://doi.org/10.1080%2F07468342.2000.11974103 Publisher: Informa UK Limited 15–21 attachment Full Text https://zero.sci-hub.se/6795/ba844bedd2d417e4393d7af19bb3dd47/bosch2000.pdf#view=FitH 2020-07-20 18:37:22 1 application/pdf conferencePaper 2009 IEEE Symposium on Computational Intelligence and Games DOI 10.1109/cig.2009.5286501 IEEE Ward C. D. Cowling P. I. Monte Carlo search applied to card selection in Magic: The Gathering September 2009 https://doi.org/10.1109%2Fcig.2009.5286501 attachment Full Text https://dacemirror.sci-hub.se/proceedings-article/dfcfc3f5502682650ac71b68af8f9b19/ward2009.pdf#view=FitH 2020-07-20 18:29:50 1 application/pdf bookSection Lecture Notes in Computer Science Springer Berlin Heidelberg Demaine Erik D. Demaine Martin L. Uehara Ryuhei Uno Takeaki Uno Yushi UNO Is Hard, Even for a Single Player 2010 https://doi.org/10.1007%2F978-3-642-13122-6_15 DOI: 10.1007/978-3-642-13122-6_15 133–144 attachment Submitted Version https://dspace.mit.edu/bitstream/1721.1/62147/1/Demaine_UNO%20is.pdf 2020-07-20 18:36:18 1 application/pdf journalArticle Information Processing Letters DOI 10.1016/j.ipl.2020.105995 Mishiba Shohei Takenaga Yasuhiko QUIXO is EXPTIME-complete July 2020 https://doi.org/10.1016%2Fj.ipl.2020.105995 Publisher: Elsevier BV 105995 attachment Full Text https://pdf.sciencedirectassets.com/271527/AIP/1-s2.0-S002001902030082X/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEHoaCXVzLWVhc3QtMSJGMEQCIFtkSBpgjQCGD2t9HhDGKXByeuMLxjq5SpZiHiVJRtD2AiBGNfyOHc5rhR9YOWBJqfm4Q4sk9A7DiAYQK4bE21l10yq0AwgyEAMaDDA1OTAwMzU0Njg2NSIMTbV17TBAQ%2BDOw4NqKpEDFYUi3wRhC%2Baj5%2FaTwaaOsbSSQ1WVXW9J%2FkDFGuUgFScfYqdG0aRaazztSFianGDgj1FEpVC%2FwLMP8LEWFghexDo2fLhZpoaNA5v8DQIvrvb839ZJGlCB9HEcbyeStsLWWrl8pM1lYBckbsmir72eSqxkPqyFfdxni2pG4HcCVuJHe6pPJwoGPGoTndv1mCghHzuk9rvPiegQ9iaKu947uL9xnhB1c7TzMUf2EGPeKuB2jm4F5duW8V3IzqQjPf3tMPSRNn8Ztv1qlO8vUhpXTsyI5dH%2BURZTqOVp0fVn4En6CRNrkv05g%2B1rxq6b6gQmlfUeAIPaTwUfI2glYGtZKNkvlkYrZKoWvHkv9XzLd3%2FKiuaeKxM9nk4hZjJqtcWwaD3Gp9yr63IUPqUZW5BI2YJHNW%2B9SIbRzBmubE0b01LVFubW9rJo3hPtgKRHPpIEIm0j%2FjoszFdpyL4chFaML0HxrCmQeh7HkvJBMvERUM%2F882V%2FBm2zHRuKsPNpLUj%2BJ%2BDh6%2FQaE0pmoIOfTbNN8xIwnaLX%2BAU67AFOxpNyX5biC5h3HLfyBGY1KrsDmnyo3bcOIqAwepos4Dw%2BlQ8II9AjVQwzEJGvd8LQ9sYaZzntH7rnuZG15wBizwUDkD2G37c91hT%2BG9hKPKHkW9jZp6XijVMHYWhd34TF6iW%2BQSM5bMzKdQaXzoilRts%2B5DaLCeYk%2Fzc2FFcSMOT3pXBXWHr%2F16lr5Sp8Gh9FS9HnwI2O8pxy6E1lqGM0wP%2FwaWBT3HgdR2tvjQzn%2BcjDDKHABtqj3oo6janO3IKOPaFzYHFqiL0DS8Pet5gUVYynm9m37o5M3%2B6y7YoaXnoq1o1goiX6Zv0S5A%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20200720T183148Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYWL2N32C3%2F20200720%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=99c8222a858071c0b65fa6dac4ea0e1b548c1a4eb23ccac06cf864c7013a7593&hash=d2c7df4c2396ff204caa66ac0553e18f1b2712399c07b6e674199859ccc1b7f9&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S002001902030082X&tid=spdf-742da070-9da3-43f1-861e-f733776129ef&sid=76772d953af62548ae38ab517857dea18c60gxrqb&type=client 2020-07-20 18:31:53 1 application/pdf bookSection Case-Based Reasoning Research and Development Springer International Publishing Woolford Michael Watson Ian SCOUT: A Case-Based Reasoning Agent for Playing Race for the Galaxy 2017 https://doi.org/10.1007%2F978-3-319-61030-6_27 DOI: 10.1007/978-3-319-61030-6_27 390–402 attachment Woolford and Watson - 2017 - SCOUT A Case-Based Reasoning Agent for Playing Ra.pdf application/pdf journalArticle 85 Mathematics Magazine DOI 10.4169/math.mag.85.2.083 2 Coleman Ben Hartshorn Kevin Game, Set, Math April 2012 https://doi.org/10.4169%2Fmath.mag.85.2.083 Publisher: Informa UK Limited 83–96 attachment Full Text https://dacemirror.sci-hub.se/journal-article/768dabc67f6adcaa34a4c087b56b4283/game-set-math-2012.pdf#view=FitH 2020-07-20 18:24:32 1 application/pdf journalArticle 125 The American Mathematical Monthly DOI 10.1080/00029890.2018.1412661 3 Glass Darren The Joy of SET February 2018 https://doi.org/10.1080%2F00029890.2018.1412661 Publisher: Informa UK Limited 284–288 attachment Full Text https://twin.sci-hub.se/6684/b949dbda2e3438aae344825abb7d0ff3/glass2018.pdf#view=FitH 2020-07-20 18:35:34 1 application/pdf bookSection Communications in Computer and Information Science Springer Berlin Heidelberg Lazarusli Irene A. Lukas Samuel Widjaja Patrick Implementation of Artificial Intelligence with 3 Different Characters of AI Player on “Monopoly Deal” Computer Game 2015 https://doi.org/10.1007%2F978-3-662-46742-8_11 DOI: 10.1007/978-3-662-46742-8_11 119–127 bookSection Computers and Games Springer Berlin Heidelberg Pawlewicz Jakub Nearly Optimal Computer Play in Multi-player Yahtzee 2011 https://doi.org/10.1007%2F978-3-642-17928-0_23 DOI: 10.1007/978-3-642-17928-0_23 250–262 conferencePaper 2007 IEEE Symposium on Computational Intelligence and Games DOI 10.1109/cig.2007.368089 IEEE Glenn James R. Computer Strategies for Solitaire Yahtzee 2007 https://doi.org/10.1109%2Fcig.2007.368089 attachment Submitted Version http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=707B7E09A9652A1E4F2AB4BF608C410A?doi=10.1.1.111.1724&rep=rep1&type=pdf 2020-07-20 18:09:04 1 application/pdf journalArticle 18 Expert Systems DOI 10.1111/1468-0394.00160 2 Maynard Ken Moss Patrick Whitehead Marcus Narayanan S. Garay Matt Brannon Nathan Kantamneni Raj Gopal Kustra Todd Modeling expert problem solving in a game of chance: a Yahtzeec case study May 2001 https://doi.org/10.1111%2F1468-0394.00160 Publisher: Wiley 88–98 attachment Full Text https://cyber.sci-hub.se/MTAuMTExMS8xNDY4LTAzOTQuMDAxNjA=/maynard2001.pdf#view=FitH 2020-07-20 18:29:00 1 application/pdf bookSection Computers and Games Springer International Publishing Oka Kazuto Matsuzaki Kiminori Systematic Selection of N-Tuple Networks for 2048 2016 https://doi.org/10.1007%2F978-3-319-50935-8_8 DOI: 10.1007/978-3-319-50935-8_8 81–92 attachment Full Text https://sci-hub.se/downloads/2020-05-25/5f/oka2016.pdf#view=FitH 2020-07-20 18:32:30 1 application/pdf conferencePaper 2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) DOI 10.1109/taai.2016.7880154 IEEE Matsuzaki Kiminori Systematic selection of N-tuple networks with consideration of interinfluence for game 2048 November 2016 https://doi.org/10.1109%2Ftaai.2016.7880154 attachment Full Text https://twin.sci-hub.se/6299/d9bbecbbec212dab7fe6e6a67213b1cb/matsuzaki2016.pdf#view=FitH 2020-07-20 18:32:39 1 application/pdf conferencePaper 2014 IEEE Conference on Computational Intelligence and Games DOI 10.1109/cig.2014.6932920 IEEE Rodgers Philip Levine John An investigation into 2048 AI strategies August 2014 https://doi.org/10.1109%2Fcig.2014.6932920 attachment Full Text https://zero.sci-hub.se/3377/2e196ce6e3cb06a636bf1ffdee8f5b6f/rodgers2014.pdf#view=FitH 2020-07-20 18:21:23 1 application/pdf journalArticle arxiv:2006.04635 Anthony Thomas Eccles Tom Tacchetti Andrea Kramár János Gemp Ian Hudson Thomas C. Porcel Nicolas Lanctot Marc Pérolat Julien Everett Richard Singh Satinder Graepel Thore Bachrach Yoram Learning to Play No-Press Diplomacy with Best Response Policy Iteration 2020 http://arxiv.org/abs/2006.04635v2 attachment Full Text https://arxiv.org/pdf/2006.04635v2.pdf 2020-07-20 18:27:18 1 application/pdf journalArticle arxiv:1909.02128 Paquette Philip Lu Yuchen Bocco Steven Smith Max O. Ortiz-Gagne Satya Kummerfeld Jonathan K. Singh Satinder Pineau Joelle Courville Aaron No Press Diplomacy: Modeling Multi-Agent Gameplay 2019 http://arxiv.org/abs/1909.02128v2 attachment Full Text https://arxiv.org/pdf/1909.02128v2.pdf 2020-07-20 18:31:04 1 application/pdf journalArticle arxiv:1902.06996 Tan Hao Hao Agent Madoff: A Heuristic-Based Negotiation Agent For The Diplomacy Strategy Game 2019 http://arxiv.org/abs/1902.06996v1 attachment Full Text https://arxiv.org/pdf/1902.06996v1.pdf 2020-07-20 18:21:06 1 application/pdf journalArticle arxiv:1807.04458 Gedda Magnus Lagerkvist Mikael Z. Butler Martin Monte Carlo Methods for the Game Kingdomino 2018 http://arxiv.org/abs/1807.04458v2 attachment Full Text https://arxiv.org/pdf/1807.04458v2.pdf 2020-07-20 18:29:18 1 application/pdf journalArticle arxiv:1909.02849 Nguyen Viet-Ha Perrot Kevin Vallet Mathieu NP-completeness of the game Kingdomino 2019 http://arxiv.org/abs/1909.02849v3 attachment Full Text https://arxiv.org/pdf/1909.02849v3.pdf 2020-07-20 18:31:12 1 application/pdf journalArticle arxiv:1912.02318 Lerer Adam Hu Hengyuan Foerster Jakob Brown Noam Improving Policies via Search in Cooperative Partially Observable Games 2019 http://arxiv.org/abs/1912.02318v1 attachment Full Text https://arxiv.org/pdf/1912.02318v1.pdf 2020-07-20 18:26:28 1 application/pdf journalArticle arxiv:1603.01911 Baffier Jean-Francois Chiu Man-Kwun Diez Yago Korman Matias Mitsou Valia Renssen André van Roeloffzen Marcel Uno Yushi Hanabi is NP-hard, Even for Cheaters who Look at Their Cards 2016 http://arxiv.org/abs/1603.01911v3 attachment Full Text https://arxiv.org/pdf/1603.01911v3.pdf 2020-07-20 18:25:31 1 application/pdf journalArticle arxiv:2004.13710 Canaan Rodrigo Gao Xianbo Togelius Julian Nealen Andy Menzel Stefan Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi 2020 http://arxiv.org/abs/2004.13710v2 attachment Full Text https://arxiv.org/pdf/2004.13710v2.pdf 2020-07-20 18:25:19 1 application/pdf journalArticle arxiv:2004.13291 Canaan Rodrigo Gao Xianbo Chung Youjin Togelius Julian Nealen Andy Menzel Stefan Evaluating the Rainbow DQN Agent in Hanabi with Unseen Partners 2020 http://arxiv.org/abs/2004.13291v1 attachment Full Text https://arxiv.org/pdf/2004.13291v1.pdf 2020-07-20 18:22:45 1 application/pdf journalArticle arxiv:2003.05119 Biderman Stella Magic: the Gathering is as Hard as Arithmetic 2020 http://arxiv.org/abs/2003.05119v1 attachment Full Text https://arxiv.org/pdf/2003.05119v1.pdf 2020-07-20 18:27:42 1 application/pdf journalArticle arxiv:1904.09828 Churchill Alex Biderman Stella Herrick Austin Magic: The Gathering is Turing Complete 2019 http://arxiv.org/abs/1904.09828v2 attachment Full Text https://arxiv.org/pdf/1904.09828v2.pdf 2020-07-20 18:27:51 1 application/pdf journalArticle arxiv:1810.03744 Zilio Felipe Prates Marcelo Neural Networks Models for Analyzing Magic: the Gathering Cards 2018 http://arxiv.org/abs/1810.03744v1 attachment Full Text https://arxiv.org/pdf/1810.03744v1.pdf 2020-07-20 18:30:42 1 application/pdf conferencePaper Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence DOI 10.1145/3396474.3396492 ACM Grichshenko Alexandr Araújo Luiz Jonatã Pires de Gimaeva Susanna Brown Joseph Alexander Using Tabu Search Algorithm for Map Generation in the Terra Mystica Tabletop Game March 2020 https://doi.org/10.1145%2F3396474.3396492 attachment Submitted Version https://arxiv.org/pdf/2006.02716 2020-07-20 18:36:31 1 application/pdf journalArticle arxiv:1009.1031 Migdał Piotr A mathematical model of the Mafia game 2010 http://arxiv.org/abs/1009.1031v3 attachment Full Text https://arxiv.org/pdf/1009.1031v3.pdf 2020-07-20 18:20:44 1 application/pdf journalArticle arxiv:1003.2851 Demaine Erik D. Demaine Martin L. Harvey Nicholas J. A. Uehara Ryuhei Uno Takeaki Uno Yushi The complexity of UNO 2010 http://arxiv.org/abs/1003.2851v3 attachment Full Text https://arxiv.org/pdf/1003.2851v3.pdf 2020-07-20 18:34:43 1 application/pdf journalArticle arxiv:1603.00928 Almanza Matteo Leucci Stefano Panconesi Alessandro Trainyard is NP-Hard 2016 http://arxiv.org/abs/1603.00928v1 attachment Full Text https://arxiv.org/pdf/1603.00928v1.pdf 2020-07-20 18:36:08 1 application/pdf journalArticle arxiv:1505.04274 Langerman Stefan Uno Yushi Threes!, Fives, 1024!, and 2048 are Hard 2015 http://arxiv.org/abs/1505.04274v1 attachment Full Text https://arxiv.org/pdf/1505.04274v1.pdf 2020-07-20 18:35:46 1 application/pdf journalArticle arxiv:1804.07396 Eppstein David Making Change in 2048 2018 http://arxiv.org/abs/1804.07396v1 attachment Full Text https://arxiv.org/pdf/1804.07396v1.pdf 2020-07-20 18:28:01 1 application/pdf journalArticle arxiv:1804.07393 Das Madhuparna Paul Goutam Analysis of the Game "2048" and its Generalization in Higher Dimensions 2018 http://arxiv.org/abs/1804.07393v2 attachment Full Text https://arxiv.org/pdf/1804.07393v2.pdf 2020-07-20 18:21:31 1 application/pdf journalArticle arxiv:1606.07374 Yeh Kun-Hao Wu I.-Chen Hsueh Chu-Hsuan Chang Chia-Chuan Liang Chao-Chin Chiang Han Multi-Stage Temporal Difference Learning for 2048-like Games 2016 http://arxiv.org/abs/1606.07374v2 attachment Full Text https://arxiv.org/pdf/1606.07374v2.pdf 2020-07-20 18:30:19 1 application/pdf journalArticle arxiv:1408.6315 Mehta Rahul 2048 is (PSPACE) Hard, but Sometimes Easy 2014 http://arxiv.org/abs/1408.6315v1 attachment Full Text https://arxiv.org/pdf/1408.6315v1.pdf 2020-07-20 18:20:36 1 application/pdf computerProgram Settlers of Catan bot trained using reinforcement learning https://jonzia.github.io/Catan/ MATLAB conferencePaper 34 Proceedings of the Annual Meeting of the Cognitive Science Society Guhe Markus Lascarides Alex Trading in a multiplayer board game: Towards an analysis of non-cooperative dialogue 2012 https://escholarship.org/uc/item/9zt506xx Issue: 34 attachment Guhe and Lascarides - 2012 - Trading in a multiplayer board game Towards an an.pdf application/pdf journalArticle POMCP with Human Preferencesin Settlers of Catan https://www.aaai.org/ocs/index.php/AIIDE/AIIDE18/paper/viewFile/18091/17217 attachment POMCP with Human Preferencesin Settlers of Catan.pdf application/pdf blogPost The impact of loaded dice in Catan https://izbicki.me/blog/how-to-cheat-at-settlers-of-catan-by-loading-the-dice-and-prove-it-with-p-values.html journalArticle Monte Carlo Tree Search in a Modern Board Game Framework https://project.dke.maastrichtuniversity.nl/games/files/bsc/Roelofs_Bsc-paper.pdf attachment Full Text https://project.dke.maastrichtuniversity.nl/games/files/bsc/Roelofs_Bsc-paper.pdf 2020-07-20 18:47:19 1 application/pdf conferencePaper Pfeiffer Michael Reinforcement Learning of Strategies for Settlers of Catan 2004 https://www.researchgate.net/publication/228728063_Reinforcement_learning_of_strategies_for_Settlers_of_Catan attachment Pfeiffer - 2004 - Reinforcement Learning of Strategies for Settlers .pdf application/pdf presentation Michael Wolf An Intelligent Artificial Player for the Game of Risk 20/04/2005 http://www.ke.tu-darmstadt.de/lehre/archiv/ss04/oberseminar/folien/Wolf_Michael-Slides.pdf attachment An Intelligent Artificial Player for the Game of R.pdf application/pdf journalArticle RISKy Business: An In-Depth Look at the Game RISK https://scholar.rose-hulman.edu/rhumj/vol3/iss2/3 attachment RISKy Business An In-Depth Look at the Game RISK.pdf application/pdf journalArticle RISK Board Game ‐ Battle Outcome Analysis http://www.c4i.gr/xgeorgio/docs/RISK-board-game%20_rev-3.pdf attachment Full Text https://www.c4i.gr/xgeorgio/docs/RISK-board-game%20_rev-3.pdf 2020-07-20 18:54:23 1 application/pdf thesis Blekinge Institute of Technology, School of Engineering, Department of Systems and Software Engineering. Olsson Fredrik A multi-agent system for playing the board game risk Risk is a game in which traditional Artificial-Intelligence methods such as for example iterative deepening and Alpha-Beta pruning can not successfully be applied due to the size of the search space. Distributed problem solving in the form of a multi-agent system might be the solution. This needs to be tested before it is possible to tell if a multi-agent system will be successful at playing Risk or not. In this thesis the development of a multi-agent system that plays Risk is explained. The system places an agent in every country on the board and uses a central agent for organizing communication. An auction mechanism is used for negotiation. The experiments show that a multi-agent solution indeed is a prosperous approach when developing a computer based player for the board game Risk. 2005 http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3781 51 Independent thesis Advanced level (degree of Master (One Year)) attachment Full Text http://bth.diva-portal.org/smash/get/diva2:831093/FULLTEXT01 2021-07-24 08:26:48 1 application/pdf attachment Full Text https://www.diva-portal.org/smash/get/diva2:831093/FULLTEXT01.pdf 2021-07-24 08:28:25 3 blogPost State Representation and Polyomino Placement for the Game Patchwork https://zayenz.se/blog/post/patchwork-modref2019-paper/ journalArticle arXiv:2001.04233 [cs] Lagerkvist Mikael Zayenz Computer Science - Artificial Intelligence State Representation and Polyomino Placement for the Game Patchwork Modern board games are a rich source of entertainment for many people, but also contain interesting and challenging structures for game playing research and implementing game playing agents. This paper studies the game Patchwork, a two player strategy game using polyomino tile drafting and placement. The core polyomino placement mechanic is implemented in a constraint model using regular constraints, extending and improving the model in (Lagerkvist, Pesant, 2008) with: explicit rotation handling; optional placements; and new constraints for resource usage. Crucial for implementing good game playing agents is to have great heuristics for guiding the search when faced with large branching factors. This paper divides placing tiles into two parts: a policy used for placing parts and an evaluation used to select among different placements. Policies are designed based on classical packing literature as well as common standard constraint programming heuristics. For evaluation, global propagation guided regret is introduced, choosing placements based on not ruling out later placements. Extensive evaluations are performed, showing the importance of using a good evaluation and that the proposed global propagation guided regret is a very effective guide. 2020-01-13 arXiv.org http://arxiv.org/abs/2001.04233 2020-07-21 10:55:58 arXiv: 2001.04233 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2001.04233.pdf 2020-07-21 10:56:09 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2001.04233 2020-07-21 10:56:13 1 text/html presentation State Representation and Polyomino Placement for the Game Patchwork https://zayenz.se/papers/Lagerkvist_ModRef_2019_Presentation.pdf attachment Full Text https://zayenz.se/papers/Lagerkvist_ModRef_2019_Presentation.pdf 2020-07-21 10:56:59 1 application/pdf journalArticle arXiv:2001.04238 [cs] Lagerkvist Mikael Zayenz Computer Science - Artificial Intelligence Nmbr9 as a Constraint Programming Challenge Modern board games are a rich source of interesting and new challenges for combinatorial problems. The game Nmbr9 is a solitaire style puzzle game using polyominoes. The rules of the game are simple to explain, but modelling the game effectively using constraint programming is hard. This abstract presents the game, contributes new generalized variants of the game suitable for benchmarking and testing, and describes a model for the presented variants. The question of the top possible score in the standard game is an open challenge. 2020-01-13 arXiv.org http://arxiv.org/abs/2001.04238 2020-07-21 10:57:58 arXiv: 2001.04238 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2001.04238.pdf 2020-07-21 10:58:01 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2001.04238 2020-07-21 10:58:02 1 text/html blogPost Nmbr9 as a Constraint Programming Challenge https://zayenz.se/blog/post/nmbr9-cp2019-abstract/ conferencePaper DOI 10.1109/CIG.2019.8848097 Goodman James Re-determinizing MCTS in Hanabi 08 2019 1-8 attachment Goodman - 2019 - Re-determinizing MCTS in Hanabi.pdf application/pdf conferencePaper ISBN 978-1-5386-4359-4 2018 IEEE Conference on Computational Intelligence and Games (CIG) DOI 10.1109/CIG.2018.8490449 Maastricht IEEE Canaan Rodrigo Shen Haotian Torrado Ruben Togelius Julian Nealen Andy Menzel Stefan Evolving Agents for the Hanabi 2018 CIG Competition 8/2018 DOI.org (Crossref) https://ieeexplore.ieee.org/document/8490449/ 2020-07-21 11:01:52 1-8 2018 IEEE Conference on Computational Intelligence and Games (CIG) attachment Submitted Version https://arxiv.org/pdf/1809.09764 2020-07-21 11:01:56 1 application/pdf bookSection 765 ISBN 978-3-319-67467-4 978-3-319-67468-1 BNAIC 2016: Artificial Intelligence Cham Springer International Publishing Bosse Tibor Bredeweg Bert van den Bergh Mark J. H. Hommelberg Anne Kosters Walter A. Spieksma Flora M. Aspects of the Cooperative Card Game Hanabi 2017 DOI.org (Crossref) http://link.springer.com/10.1007/978-3-319-67468-1_7 2020-07-21 11:02:26 Series Title: Communications in Computer and Information Science DOI: 10.1007/978-3-319-67468-1_7 93-105 attachment Full Text https://twin.sci-hub.se/6548/49fca9bfed767f739defcd030c004bdb/vandenbergh2017.pdf#view=FitH 2020-07-21 11:02:31 1 application/pdf bookSection 10664 ISBN 978-3-319-71648-0 978-3-319-71649-7 Advances in Computer Games Cham Springer International Publishing Winands Mark H.M. van den Herik H. Jaap Kosters Walter A. Bouzy Bruno Playing Hanabi Near-Optimally 2017 DOI.org (Crossref) http://link.springer.com/10.1007/978-3-319-71649-7_5 2020-07-21 11:02:53 Series Title: Lecture Notes in Computer Science DOI: 10.1007/978-3-319-71649-7_5 51-62 conferencePaper ISBN 978-1-5386-3233-8 2017 IEEE Conference on Computational Intelligence and Games (CIG) DOI 10.1109/CIG.2017.8080417 New York, NY, USA IEEE Eger Markus Martens Chris Cordoba Marcela Alfaro An intentional AI for hanabi 8/2017 DOI.org (Crossref) http://ieeexplore.ieee.org/document/8080417/ 2020-07-21 11:03:36 68-75 2017 IEEE Conference on Computational Intelligence and Games (CIG) attachment Full Text https://zero.sci-hub.se/6752/bcf6e994ee7503ab821bd67848727b05/eger2017.pdf#view=FitH 2020-07-21 11:03:40 1 application/pdf conferencePaper Osawa Hirotaka Solving Hanabi: Estimating Hands by Opponent's Actions in Cooperative Game with Incomplete Information A unique behavior of humans is modifying one’s unobservable behavior based on the reaction of others for cooperation. We used a card game called Hanabi as an evaluation task of imitating human reflective intelligence with artificial intelligence. Hanabi is a cooperative card game with incomplete information. A player cooperates with an opponent in building several card sets constructed with the same color and ordered numbers. However, like a blind man's bluff, each player sees the cards of all other players except his/her own. Also, communication between players is restricted to information about the same numbers and colors, and the player is required to read his/his opponent's intention with the opponent's hand, estimate his/her cards with incomplete information, and play one of them for building a set. We compared human play with several simulated strategies. The results indicate that the strategy with feedbacks from simulated opponent's viewpoints achieves more score than other strategies. 2015 https://aaai.org/ocs/index.php/WS/AAAIW15/paper/view/10167 AAAI Workshops attachment Osawa - 2015 - Solving Hanabi Estimating Hands by Opponent's Act.pdf application/pdf journalArticle Cape Cod Eger Markus Martens Chris A Browser-based Interface for the Exploration and Evaluation of Hanabi AIs 2017 en Zotero http://fdg2017.org/papers/FDG2017_demo_Hanabi.pdf 4 attachment Eger and Martens - 2017 - A Browser-based Interface for the Exploration and .pdf application/pdf journalArticle Gottwald Eva Tallula Eger Markus Martens Chris I see what you see: Integrating eye tracking into Hanabi playing agents Humans’ eye movements convey a lot of information about their intentions, often unconsciously. Intelligent agents that cooperate with humans in various domains can benefit from interpreting this information. This paper contains a preliminary look at how eye tracking could be useful for agents that play the cooperative card game Hanabi with human players. We outline several situations in which an AI agent can utilize gaze information, and present an outlook on how we plan to integrate this with reimplementations of contemporary Hanabi agents. en Zotero 4 attachment Gottwald et al. - I see what you see Integrating eye tracking into .pdf application/pdf computerProgram State of the art Hanabi bots + simulation framework in rust https://github.com/WuTheFWasThat/hanabi.rs computerProgram A strategy simulator for the well-known cooperative card game Hanabi https://github.com/rjtobin/HanSim computerProgram A framework for writing bots that play Hanabi https://github.com/Quuxplusone/Hanabi journalArticle Ludic Language Pedagogy Ludic Language Pedagogy deHaan Jonathan Jidoukan Jenga: Teaching English through remixing games and game rules Let students play simple games in their L1. It’s ok! Then: You, the teacher, can help them critique the game in their L2. You, the teacher, can help them change the game in their L2. You, the teacher, can help them develop themselves. #dropthestick #dropthecarrot #bringmeaning 2020-04-15 Teaching English through remixing games and game rules https://www.llpjournal.org/2020/04/13/jidokan-jenga.html 📍 What is this? This is a recollection of a short lesson with some children. I used Jenga and a dictionary. 📍 Why did you make it? I want to show language teachers that simple games, and playing simple games in students’ first language can be a great foundation for helping students learn new vocabulary, think critically, and exercise creativity. 📍 Why is it radical? I taught using a simple board game (at a time when video games are over-focused on in research). I show what the learning looks like (I include a photo). The teaching and learning didn’t occur in a laboratory setting, but in the wild (in a community center). I focused on the learning around games. 📍 Who is it for? Language teachers can easily implement this lesson using Jenga or any other game. Language researchers can expand on the translating and remixing potential around games. attachment deHaan - 2020 - Jidoukan Jenga Teaching English through remixing .pdf application/pdf journalArticle Heron Michael James Belford Pauline Helen Reid Hayley Crabb Michael Meeple Centred Design: A Heuristic Toolkit for Evaluating the Accessibility of Tabletop Games 6/2018 en Meeple Centred Design DOI.org (Crossref) http://link.springer.com/10.1007/s40869-018-0057-8 2020-07-28 09:08:52 97-114 7 The Computer Games Journal DOI 10.1007/s40869-018-0057-8 2 Comput Game J ISSN 2052-773X attachment Full Text https://link.springer.com/content/pdf/10.1007/s40869-018-0057-8.pdf 2020-07-28 09:08:55 1 application/pdf journalArticle 7 The Computer Games Journal DOI 10.1007/s40869-018-0056-9 2 Comput Game J ISSN 2052-773X Heron Michael James Belford Pauline Helen Reid Hayley Crabb Michael Eighteen Months of Meeple Like Us: An Exploration into the State of Board Game Accessibility 6/2018 en Eighteen Months of Meeple Like Us DOI.org (Crossref) http://link.springer.com/10.1007/s40869-018-0056-9 2020-07-28 09:09:05 75-95 attachment Full Text https://link.springer.com/content/pdf/10.1007/s40869-018-0056-9.pdf 2020-07-28 09:09:08 1 application/pdf thesis Utrecht University Andel Daniël On the complexity of Hive It is shown that for an arbitrary position of a Hive game where both players have the same set of N pieces it is PSPACE-hard to determine whether one of the players has a winning strategy. The proof is done by reducing the known PSPACE-complete set of true quantified boolean formulas to a game concerning these formulas, then to the game generalised geography, then to a version of that game with the restriction of having only nodes with maximum degree 3, and finally to generalised Hive. This thesis includes a short introduction to the subject of computational complexity. May 2020 en-US On the complexity of Hive https://dspace.library.uu.nl/handle/1874/396955 33 Bachelor thesis attachment Andel - 2020 - On the complexity of Hive.pdf application/pdf journalArticle arXiv:2010.00048 [cs] Kunda Maithilee Rabkina Irina Computer Science - Artificial Intelligence Creative Captioning: An AI Grand Challenge Based on the Dixit Board Game We propose a new class of "grand challenge" AI problems that we call creative captioning---generating clever, interesting, or abstract captions for images, as well as understanding such captions. Creative captioning draws on core AI research areas of vision, natural language processing, narrative reasoning, and social reasoning, and across all these areas, it requires sophisticated uses of common sense and cultural knowledge. In this paper, we analyze several specific research problems that fall under creative captioning, using the popular board game Dixit as both inspiration and proposed testing ground. We expect that Dixit could serve as an engaging and motivating benchmark for creative captioning across numerous AI research communities for the coming 1-2 decades. 2020-09-30 Creative Captioning arXiv.org http://arxiv.org/abs/2010.00048 2020-10-12 04:03:28 arXiv: 2010.00048 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2010.00048.pdf 2020-10-12 04:03:46 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2010.00048 2020-10-12 04:03:53 1 text/html computerProgram Shobu AI Playground https://github.com/JayWalker512/Shobu webpage Shobu randomly played games dataset https://www.kaggle.com/bsfoltz/shobu-randomly-played-games-104k conferencePaper ISBN 978-1-4503-5319-9 Proceedings of the International Conference on the Foundations of Digital Games - FDG '17 DOI 10.1145/3102071.3102105 Hyannis, Massachusetts ACM Press de Mesentier Silva Fernando Lee Scott Togelius Julian Nealen Andy AI-based playtesting of contemporary board games 2017 en DOI.org (Crossref) http://dl.acm.org/citation.cfm?doid=3102071.3102105 2020-10-12 04:09:30 1-10 the International Conference attachment Full Text https://twin.sci-hub.se/6553/d80b9cdf7f993e1137d0b129dec94e6d/demesentiersilva2017.pdf#view=FitH 2020-10-12 04:09:38 1 application/pdf attachment PDF http://game.engineering.nyu.edu/wp-content/uploads/2017/06/ticket-ride-fdg2017-camera-ready.pdf 2020-10-12 04:13:00 3 computerProgram Copley Rowan Materials for Ticket to Ride Seattle and a framework for making more game boards https://github.com/dovinmu/ttr_generator report Nguyen Cuong Dinjian Daniel The Difficulty of Learning Ticket to Ride Ticket to Ride is a very popular, award-winning board-game where you try toscore the most points while building a railway spanning cities in America. For acomputer to learn to play this game is very difficult due to the vast state-actionspace. This project will explain why featurizing your state, and implementingcurriculum learning can help agents learn as state-action spaces grow too largefor traditional learning methods to be effective. https://www.eecs.tufts.edu/~jsinapov/teaching/comp150_RL/reports/Nguyen_Dinjian_report.pdf attachment Full Text https://www.eecs.tufts.edu/~jsinapov/teaching/comp150_RL/reports/Nguyen_Dinjian_report.pdf 2021-07-24 08:19:13 1 application/pdf conferencePaper ISBN 978-1-4503-6571-0 Proceedings of the 13th International Conference on the Foundations of Digital Games DOI 10.1145/3235765.3235813 Malmö Sweden ACM de Mesentier Silva Fernando Lee Scott Togelius Julian Nealen Andy Evolving maps and decks for ticket to ride 2018-08-07 en DOI.org (Crossref) https://dl.acm.org/doi/10.1145/3235765.3235813 2020-10-12 04:12:33 1-7 FDG '18: Foundations of Digital Games 2018 attachment Full Text https://twin.sci-hub.se/7128/24e28b0429626f565aafd93768332e73/demesentiersilva2018.pdf#view=FitH 2020-10-12 04:12:36 1 application/pdf journalArticle arXiv:2008.07079 [cs, stat] Gendre Quentin Kaneko Tomoyuki Computer Science - Artificial Intelligence Computer Science - Machine Learning Statistics - Machine Learning Playing Catan with Cross-dimensional Neural Network Catan is a strategic board game having interesting properties, including multi-player, imperfect information, stochastic, complex state space structure (hexagonal board where each vertex, edge and face has its own features, cards for each player, etc), and a large action space (including negotiation). Therefore, it is challenging to build AI agents by Reinforcement Learning (RL for short), without domain knowledge nor heuristics. In this paper, we introduce cross-dimensional neural networks to handle a mixture of information sources and a wide variety of outputs, and empirically demonstrate that the network dramatically improves RL in Catan. We also show that, for the first time, a RL agent can outperform jsettler, the best heuristic agent available. 2020-08-17 arXiv.org http://arxiv.org/abs/2008.07079 2020-10-12 04:19:57 arXiv: 2008.07079 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2008.07079.pdf 2020-10-12 04:20:04 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2008.07079 2020-10-12 04:20:10 1 text/html conferencePaper ISBN 978-1-4503-8878-8 11th Hellenic Conference on Artificial Intelligence DOI 10.1145/3411408.3411413 Athens Greece ACM Theodoridis Alexios Chalkiadakis Georgios Monte Carlo Tree Search for the Game of Diplomacy 2020-09-02 en DOI.org (Crossref) https://dl.acm.org/doi/10.1145/3411408.3411413 2020-10-12 04:20:38 16-25 SETN 2020: 11th Hellenic Conference on Artificial Intelligence journalArticle Eger Markus Martens Chris Sauma Chacon Pablo Alfaro Cordoba Marcela Hidalgo Cespedes Jeisson Operationalizing Intentionality to Play Hanabi with Human Players 2020 DOI.org (Crossref) https://ieeexplore.ieee.org/document/9140404/ 2020-11-26 08:48:44 1-1 IEEE Transactions on Games DOI 10.1109/TG.2020.3009359 IEEE Trans. Games ISSN 2475-1502, 2475-1510 attachment Full Text https://sci-hub.se/downloads/2020-08-17/f1/eger2020.pdf?rand=5fbf6bef76c6b#view=FitH 2020-11-26 08:48:52 1 application/pdf journalArticle 16 Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 1 AIIDE Canaan Rodrigo Gao Xianbo Chung Youjin Togelius Julian Nealen Andy Menzel Stefan Behavioral Evaluation of Hanabi Rainbow DQN Agents and Rule-Based Agents <p class="abstract">Hanabi is a multiplayer cooperative card game, where only your partners know your cards. All players succeed or fail together. This makes the game an excellent testbed for studying collaboration. Recently, it has been shown that deep neural networks can be trained through self-play to play the game very well. However, such agents generally do not play well with others. In this paper, we investigate the consequences of training Rainbow DQN agents with human-inspired rule-based agents. We analyze with which agents Rainbow agents learn to play well, and how well playing skill transfers to agents they were not trained with. We also analyze patterns of communication between agents to elucidate how collaboration happens. A key finding is that while most agents only learn to play well with partners seen during training, one particular agent leads the Rainbow algorithm towards a much more general policy. The metrics and hypotheses advanced in this paper can be used for further study of collaborative agents.</p> October 1, 2020 https://ojs.aaai.org/index.php/AIIDE/article/view/7404 2020-11-26 Section: Full Oral Papers 31-37 attachment View PDF https://ojs.aaai.org/index.php/AIIDE/article/view/7404/7333 2020-11-26 08:52:38 3 conferencePaper 2020第82回全国大会講演論文集 ひい とう 市来 正裕 中里 研一 Playing mini-Hanabi card game with Q-learning February 2020 http://id.nii.ac.jp/1001/00205046/ Issue: 1 41–42 attachment View PDF https://ipsj.ixsq.nii.ac.jp/ej/?action=repository_uri&item_id=205142&file_id=1&file_no=1 2020-11-26 08:54:47 3 journalArticle arXiv:2005.07156 [cs] Reinhardt Jack Computer Science - Artificial Intelligence Computer Science - Multiagent Systems Competing in a Complex Hidden Role Game with Information Set Monte Carlo Tree Search Advances in intelligent game playing agents have led to successes in perfect information games like Go and imperfect information games like Poker. The Information Set Monte Carlo Tree Search (ISMCTS) family of algorithms outperforms previous algorithms using Monte Carlo methods in imperfect information games. In this paper, Single Observer Information Set Monte Carlo Tree Search (SO-ISMCTS) is applied to Secret Hitler, a popular social deduction board game that combines traditional hidden role mechanics with the randomness of a card deck. This combination leads to a more complex information model than the hidden role and card deck mechanics alone. It is shown in 10108 simulated games that SO-ISMCTS plays as well as simpler rule based agents, and demonstrates the potential of ISMCTS algorithms in complicated information set domains. 2020-05-14 arXiv.org http://arxiv.org/abs/2005.07156 2020-11-26 09:00:33 arXiv: 2005.07156 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2005.07156.pdf 2020-11-26 09:01:03 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2005.07156 2020-11-26 09:01:10 1 text/html journalArticle arXiv:2009.12974 [cs] Ameneyro Fred Valdez Galvan Edgar Morales Anger Fernando Kuri Computer Science - Artificial Intelligence Playing Carcassonne with Monte Carlo Tree Search Monte Carlo Tree Search (MCTS) is a relatively new sampling method with multiple variants in the literature. They can be applied to a wide variety of challenging domains including board games, video games, and energy-based problems to mention a few. In this work, we explore the use of the vanilla MCTS and the MCTS with Rapid Action Value Estimation (MCTS-RAVE) in the game of Carcassonne, a stochastic game with a deceptive scoring system where limited research has been conducted. We compare the strengths of the MCTS-based methods with the Star2.5 algorithm, previously reported to yield competitive results in the game of Carcassonne when a domain-specific heuristic is used to evaluate the game states. We analyse the particularities of the strategies adopted by the algorithms when they share a common reward system. The MCTS-based methods consistently outperformed the Star2.5 algorithm given their ability to find and follow long-term strategies, with the vanilla MCTS exhibiting a more robust game-play than the MCTS-RAVE. 2020-10-04 arXiv.org http://arxiv.org/abs/2009.12974 2021-01-02 18:13:09 arXiv: 2009.12974 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2009.12974.pdf 2021-01-02 18:13:12 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2009.12974 2021-01-02 18:13:17 1 text/html journalArticle arXiv:2007.15895 [cs] Tanaka Satoshi Bonnet François Tixeuil Sébastien Tamura Yasumasa Computer Science - Computer Science and Game Theory Quixo Is Solved Quixo is a two-player game played on a 5$\times$5 grid where the players try to align five identical symbols. Specifics of the game require the usage of novel techniques. Using a combination of value iteration and backward induction, we propose the first complete analysis of the game. We describe memory-efficient data structures and algorithmic optimizations that make the game solvable within reasonable time and space constraints. Our main conclusion is that Quixo is a Draw game. The paper also contains the analysis of smaller boards and presents some interesting states extracted from our computations. 2020-07-31 arXiv.org http://arxiv.org/abs/2007.15895 2021-01-02 18:17:10 arXiv: 2007.15895 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2007.15895.pdf 2021-01-02 18:17:17 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2007.15895 2021-01-02 18:17:21 1 text/html journalArticle arXiv:2006.02353 [cs] Bertholon Guillaume Géraud-Stewart Rémi Kugelmann Axel Lenoir Théo Naccache David Computer Science - Computer Science and Game Theory At Most 43 Moves, At Least 29: Optimal Strategies and Bounds for Ultimate Tic-Tac-Toe Ultimate Tic-Tac-Toe is a variant of the well known tic-tac-toe (noughts and crosses) board game. Two players compete to win three aligned "fields", each of them being a tic-tac-toe game. Each move determines which field the next player must play in. We show that there exist a winning strategy for the first player, and therefore that there exist an optimal winning strategy taking at most 43 moves; that the second player can hold on at least 29 rounds; and identify any optimal strategy's first two moves. 2020-06-06 At Most 43 Moves, At Least 29 arXiv.org http://arxiv.org/abs/2006.02353 2021-01-02 18:17:55 arXiv: 2006.02353 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2006.02353.pdf 2021-01-02 18:17:57 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2006.02353 2021-01-02 18:18:02 1 text/html journalArticle arXiv:2004.00377 [cs] Muller-Brockhausen Matthias Preuss Mike Plaat Aske Computer Science - Artificial Intelligence A New Challenge: Approaching Tetris Link with AI Decades of research have been invested in making computer programs for playing games such as Chess and Go. This paper focuses on a new game, Tetris Link, a board game that is still lacking any scientific analysis. Tetris Link has a large branching factor, hampering a traditional heuristic planning approach. We explore heuristic planning and two other approaches: Reinforcement Learning, Monte Carlo tree search. We document our approach and report on their relative performance in a tournament. Curiously, the heuristic approach is stronger than the planning/learning approaches. However, experienced human players easily win the majority of the matches against the heuristic planning AIs. We, therefore, surmise that Tetris Link is more difficult than expected. We offer our findings to the community as a challenge to improve upon. 2020-04-01 A New Challenge arXiv.org http://arxiv.org/abs/2004.00377 2021-01-02 18:18:26 arXiv: 2004.00377 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2004.00377.pdf 2021-01-02 18:18:32 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2004.00377 2021-01-02 18:18:38 1 text/html journalArticle arXiv:1511.08099 [cs] Cuayáhuitl Heriberto Keizer Simon Lemon Oliver Computer Science - Artificial Intelligence Computer Science - Machine Learning Strategic Dialogue Management via Deep Reinforcement Learning Artificially intelligent agents equipped with strategic skills that can negotiate during their interactions with other natural or artificial agents are still underdeveloped. This paper describes a successful application of Deep Reinforcement Learning (DRL) for training intelligent agents with strategic conversational skills, in a situated dialogue setting. Previous studies have modelled the behaviour of strategic agents using supervised learning and traditional reinforcement learning techniques, the latter using tabular representations or learning with linear function approximation. In this study, we apply DRL with a high-dimensional state space to the strategic board game of Settlers of Catan---where players can offer resources in exchange for others and they can also reply to offers made by other players. Our experimental results report that the DRL-based learnt policies significantly outperformed several baselines including random, rule-based, and supervised-based behaviours. The DRL-based policy has a 53% win rate versus 3 automated players (`bots'), whereas a supervised player trained on a dialogue corpus in this setting achieved only 27%, versus the same 3 bots. This result supports the claim that DRL is a promising framework for training dialogue systems, and strategic agents with negotiation abilities. 2015-11-25 arXiv.org http://arxiv.org/abs/1511.08099 2021-01-02 18:29:38 arXiv: 1511.08099 attachment arXiv Fulltext PDF https://arxiv.org/pdf/1511.08099.pdf 2021-01-02 18:29:43 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/1511.08099 2021-01-02 18:29:50 1 text/html conferencePaper Applying Neural Networks and Genetic Programming to the Game Lost Cities https://minds.wisconsin.edu/bitstream/handle/1793/79080/LydeenSpr18.pdf?sequence=1&isAllowed=y attachment LydeenSpr18.pdf https://minds.wisconsin.edu/bitstream/handle/1793/79080/LydeenSpr18.pdf 2021-06-12 17:03:24 3 report A summary of a dissertation on Azul https://old.reddit.com/r/boardgames/comments/hxodaf/update_i_wrote_my_dissertation_on_azul/ conferencePaper Ceramic: A research environment based on the multi-player strategic board game Azul https://ipsj.ixsq.nii.ac.jp/ej/?action=repository_action_common_download&item_id=207669&item_no=1&attribute_id=1&file_no=1 computerProgram Ceramic: A research environment based on the multi-player strategic board game Azul https://github.com/Swynfel/ceramic conferencePaper ISBN 978-1-4799-2198-0 978-1-4799-2199-7 2013 International Conference on Field-Programmable Technology (FPT) DOI 10.1109/FPT.2013.6718426 Kyoto, Japan IEEE Yoza Takashi Moriwaki Retsu Torigai Yuki Kamikubo Yuki Kubota Takayuki Watanabe Takahiro Fujimori Takumi Ito Hiroyuki Seo Masato Akagi Kouta Yamaji Yuichiro Watanabe Minoru FPGA Blokus Duo Solver using a massively parallel architecture 12/2013 DOI.org (Crossref) http://ieeexplore.ieee.org/document/6718426/ 2021-06-28 14:38:57 494-497 2013 International Conference on Field-Programmable Technology (FPT) attachment Full Text https://zero.sci-hub.se/2654/a4d3e713290066b6db7db1d9eedd194e/yoza2013.pdf#view=FitH 2021-06-28 14:39:08 1 application/pdf conferencePaper ISBN 978-1-4799-0565-2 978-1-4799-0562-1 978-1-4799-0563-8 The 17th CSI International Symposium on Computer Architecture & Digital Systems (CADS 2013) DOI 10.1109/CADS.2013.6714256 Tehran, Iran IEEE Jahanshahi Ali Taram Mohammad Kazem Eskandari Nariman Blokus Duo game on FPGA 10/2013 DOI.org (Crossref) http://ieeexplore.ieee.org/document/6714256/ 2021-06-28 14:39:04 149-152 2013 17th CSI International Symposium on Computer Architecture and Digital Systems (CADS) attachment Full Text https://zero.sci-hub.se/3228/9ae6ca1efab5a2ebb63dd4e22a13bf04/jahanshahi2013.pdf#view=FitH 2021-06-28 14:39:07 1 application/pdf journalArticle The World Wide Web Conference DOI 10.1145/3308558.3314131 Hsu Chao-Chun Chen Yu-Hua Chen Zi-Yuan Lin Hsin-Yu Huang Ting-Hao 'Kenneth' Ku Lun-Wei Computer Science - Computation and Language Dixit: Interactive Visual Storytelling via Term Manipulation In this paper, we introduce Dixit, an interactive visual storytelling system that the user interacts with iteratively to compose a short story for a photo sequence. The user initiates the process by uploading a sequence of photos. Dixit first extracts text terms from each photo which describe the objects (e.g., boy, bike) or actions (e.g., sleep) in the photo, and then allows the user to add new terms or remove existing terms. Dixit then generates a short story based on these terms. Behind the scenes, Dixit uses an LSTM-based model trained on image caption data and FrameNet to distill terms from each image and utilizes a transformer decoder to compose a context-coherent story. Users change images or terms iteratively with Dixit to create the most ideal story. Dixit also allows users to manually edit and rate stories. The proposed procedure opens up possibilities for interpretable and controllable visual storytelling, allowing users to understand the story formation rationale and to intervene in the generation process. 2019-05-13 Dixit arXiv.org http://arxiv.org/abs/1903.02230 2021-06-28 14:40:29 arXiv: 1903.02230 3531-3535 attachment arXiv Fulltext PDF https://arxiv.org/pdf/1903.02230.pdf 2021-06-28 14:40:38 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/1903.02230 2021-06-28 14:40:43 1 text/html computerProgram Dominion Simulator https://dominionsimulator.wordpress.com/f-a-q/ computerProgram Dominion Simulator Source Code https://github.com/mikemccllstr/dominionstats/ blogPost Best and worst openings in Dominion http://councilroom.com/openings blogPost Optimal Card Ratios in Dominion http://councilroom.com/optimal_card_ratios blogPost Card Winning Stats on Dominion Server http://councilroom.com/supply_win forumPost Dominion Strategy Forum http://forum.dominionstrategy.com/index.php journalArticle arXiv:1811.11273 [cs] Bendekgey Henry Computer Science - Artificial Intelligence Clustering Player Strategies from Variable-Length Game Logs in Dominion We present a method for encoding game logs as numeric features in the card game Dominion. We then run the manifold learning algorithm t-SNE on these encodings to visualize the landscape of player strategies. By quantifying game states as the relative prevalence of cards in a player's deck, we create visualizations that capture qualitative differences in player strategies. Different ways of deviating from the starting game state appear as different rays in the visualization, giving it an intuitive explanation. This is a promising new direction for understanding player strategies across games that vary in length. 2018-12-12 arXiv.org http://arxiv.org/abs/1811.11273 2021-06-28 14:43:21 arXiv: 1811.11273 attachment arXiv Fulltext PDF https://arxiv.org/pdf/1811.11273.pdf 2021-06-28 14:43:27 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/1811.11273 2021-06-28 14:43:31 1 text/html computerProgram Hanabi Open Agent Dataset https://github.com/aronsar/hoad conferencePaper Hanabi Open Agent Dataset https://dl.acm.org/doi/10.5555/3463952.3464188 journalArticle arXiv:2010.02923 [cs] Gray Jonathan Lerer Adam Bakhtin Anton Brown Noam Computer Science - Artificial Intelligence Computer Science - Machine Learning Computer Science - Computer Science and Game Theory Human-Level Performance in No-Press Diplomacy via Equilibrium Search Prior AI breakthroughs in complex games have focused on either the purely adversarial or purely cooperative settings. In contrast, Diplomacy is a game of shifting alliances that involves both cooperation and competition. For this reason, Diplomacy has proven to be a formidable research challenge. In this paper we describe an agent for the no-press variant of Diplomacy that combines supervised learning on human data with one-step lookahead search via regret minimization. Regret minimization techniques have been behind previous AI successes in adversarial games, most notably poker, but have not previously been shown to be successful in large-scale games involving cooperation. We show that our agent greatly exceeds the performance of past no-press Diplomacy bots, is unexploitable by expert humans, and ranks in the top 2% of human players when playing anonymous games on a popular Diplomacy website. 2021-05-03 arXiv.org http://arxiv.org/abs/2010.02923 2021-06-28 15:28:02 arXiv: 2010.02923 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2010.02923.pdf 2021-06-28 15:28:18 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2010.02923 2021-06-28 15:28:22 1 text/html journalArticle arXiv:1708.01503 [math] Akiyama Rika Abe Nozomi Fujita Hajime Inaba Yukie Hataoka Mari Ito Shiori Seita Satomi 55A20 (Primary), 05A99 (Secondary) Mathematics - Combinatorics Mathematics - Geometric Topology Mathematics - History and Overview Maximum genus of the Jenga like configurations We treat the boundary of the union of blocks in the Jenga game as a surface with a polyhedral structure and consider its genus. We generalize the game and determine the maximum genus of the generalized game. 2018-08-31 arXiv.org http://arxiv.org/abs/1708.01503 2021-06-28 15:28:12 arXiv: 1708.01503 attachment arXiv Fulltext PDF https://arxiv.org/pdf/1708.01503.pdf 2021-06-28 15:28:21 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/1708.01503 2021-06-28 15:28:24 1 text/html journalArticle arXiv:1905.08617 [cs] Bai Chongyang Bolonkin Maksim Burgoon Judee Chen Chao Dunbar Norah Singh Bharat Subrahmanian V. S. Wu Zhe Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition Automatic Long-Term Deception Detection in Group Interaction Videos Most work on automated deception detection (ADD) in video has two restrictions: (i) it focuses on a video of one person, and (ii) it focuses on a single act of deception in a one or two minute video. In this paper, we propose a new ADD framework which captures long term deception in a group setting. We study deception in the well-known Resistance game (like Mafia and Werewolf) which consists of 5-8 players of whom 2-3 are spies. Spies are deceptive throughout the game (typically 30-65 minutes) to keep their identity hidden. We develop an ensemble predictive model to identify spies in Resistance videos. We show that features from low-level and high-level video analysis are insufficient, but when combined with a new class of features that we call LiarRank, produce the best results. We achieve AUCs of over 0.70 in a fully automated setting. Our demo can be found at http://home.cs.dartmouth.edu/~mbolonkin/scan/demo/ 2019-06-15 arXiv.org http://arxiv.org/abs/1905.08617 2021-06-28 15:32:49 arXiv: 1905.08617 attachment arXiv Fulltext PDF https://arxiv.org/pdf/1905.08617.pdf 2021-06-28 15:32:54 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/1905.08617 2021-06-28 15:32:58 1 text/html bookSection 10068 ISBN 978-3-319-50934-1 978-3-319-50935-8 Computers and Games Cham Springer International Publishing Plaat Aske Kosters Walter van den Herik Jaap Bi Xiaoheng Tanaka Tetsuro Human-Side Strategies in the Werewolf Game Against the Stealth Werewolf Strategy 2016 DOI.org (Crossref) http://link.springer.com/10.1007/978-3-319-50935-8_9 2021-06-28 15:32:54 Series Title: Lecture Notes in Computer Science DOI: 10.1007/978-3-319-50935-8_9 93-102 attachment Full Text https://sci-hub.se/downloads/2019-01-26//f7/bi2016.pdf#view=FitH 2021-06-28 15:33:08 1 application/pdf journalArticle arXiv:0804.0071 [math] Yao Erlin 65C20 91-01 Mathematics - Probability A Theoretical Study of Mafia Games Mafia can be described as an experiment in human psychology and mass hysteria, or as a game between informed minority and uninformed majority. Focus on a very restricted setting, Mossel et al. [to appear in Ann. Appl. Probab. Volume 18, Number 2] showed that in the mafia game without detectives, if the civilians and mafias both adopt the optimal randomized strategy, then the two groups have comparable probabilities of winning exactly when the total player size is R and the mafia size is of order Sqrt(R). They also proposed a conjecture which stated that this phenomenon should be valid in a more extensive framework. In this paper, we first indicate that the main theorem given by Mossel et al. [to appear in Ann. Appl. Probab. Volume 18, Number 2] can not guarantee their conclusion, i.e., the two groups have comparable winning probabilities when the mafia size is of order Sqrt(R). Then we give a theorem which validates the correctness of their conclusion. In the last, by proving the conjecture proposed by Mossel et al. [to appear in Ann. Appl. Probab. Volume 18, Number 2], we generalize the phenomenon to a more extensive framework, of which the mafia game without detectives is only a special case. 2008-04-01 arXiv.org http://arxiv.org/abs/0804.0071 2021-06-28 15:33:04 arXiv: 0804.0071 attachment arXiv Fulltext PDF https://arxiv.org/pdf/0804.0071.pdf 2021-06-28 15:33:07 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/0804.0071 2021-06-28 15:33:10 1 text/html bookSection 11302 ISBN 978-3-030-04178-6 978-3-030-04179-3 Neural Information Processing Cham Springer International Publishing Cheng Long Leung Andrew Chi Sing Ozawa Seiichi Zilio Felipe Prates Marcelo Lamb Luis Neural Networks Models for Analyzing Magic: The Gathering Cards 2018 Neural Networks Models for Analyzing Magic DOI.org (Crossref) http://link.springer.com/10.1007/978-3-030-04179-3_20 2021-06-28 15:33:26 Series Title: Lecture Notes in Computer Science DOI: 10.1007/978-3-030-04179-3_20 227-239 attachment Submitted Version https://arxiv.org/pdf/1810.03744 2021-06-28 15:33:36 1 application/pdf conferencePaper The Complexity of Deciding Legality of a Single Step of Magic: The Gathering https://livrepository.liverpool.ac.uk/3029568/ conferencePaper Magic: The Gathering in Common Lisp https://vixra.org/abs/2001.0065 computerProgram Magic: The Gathering in Common Lisp https://github.com/jeffythedragonslayer/maglisp thesis Mathematical programming and Magic: The Gathering https://commons.lib.niu.edu/handle/10843/19194 conferencePaper Deck Construction Strategies for Magic: The Gathering https://www.doi.org/10.1685/CSC06077 thesis Deckbuilding in Magic: The Gathering Using a Genetic Algorithm https://doi.org/11250/2462429 report Magic: The Gathering Deck Performance Prediction http://cs229.stanford.edu/proj2012/HauPlotkinTran-MagicTheGatheringDeckPerformancePrediction.pdf computerProgram A constraint programming based solver for Modern Art https://github.com/captn3m0/modernart journalArticle arXiv:2103.00683 [cs] Haliem Marina Bonjour Trevor Alsalem Aala Thomas Shilpa Li Hongyu Aggarwal Vaneet Bhargava Bharat Kejriwal Mayank Computer Science - Artificial Intelligence Computer Science - Machine Learning Learning Monopoly Gameplay: A Hybrid Model-Free Deep Reinforcement Learning and Imitation Learning Approach Learning how to adapt and make real-time informed decisions in dynamic and complex environments is a challenging problem. To learn this task, Reinforcement Learning (RL) relies on an agent interacting with an environment and learning through trial and error to maximize the cumulative sum of rewards received by it. In multi-player Monopoly game, players have to make several decisions every turn which involves complex actions, such as making trades. This makes the decision-making harder and thus, introduces a highly complicated task for an RL agent to play and learn its winning strategies. In this paper, we introduce a Hybrid Model-Free Deep RL (DRL) approach that is capable of playing and learning winning strategies of the popular board game, Monopoly. To achieve this, our DRL agent (1) starts its learning process by imitating a rule-based agent (that resembles the human logic) to initialize its policy, (2) learns the successful actions, and improves its policy using DRL. Experimental results demonstrate an intelligent behavior of our proposed agent as it shows high win rates against different types of agent-players. 2021-02-28 Learning Monopoly Gameplay arXiv.org http://arxiv.org/abs/2103.00683 2021-06-28 15:48:08 arXiv: 2103.00683 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2103.00683.pdf 2021-06-28 15:48:19 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2103.00683 2021-06-28 15:48:23 1 text/html conferencePaper ISBN 978-0-7803-7203-0 Proceedings 2001 IEEE International Symposium on Computational Intelligence in Robotics and Automation (Cat. No.01EX515) DOI 10.1109/CIRA.2001.1013210 Banff, Alta., Canada IEEE Yasumura Y. Oguchi K. Nitta K. Negotiation strategy of agents in the MONOPOLY game 2001 DOI.org (Crossref) http://ieeexplore.ieee.org/document/1013210/ 2021-06-28 15:49:10 277-281 2001 International Symposium on Computational Intelligence in Robotics and Automation attachment Full Text https://moscow.sci-hub.se/3317/19346a5b777c1582800b51ee3a7cf5ed/negotiation-strategy-of-agents-in-the-monopoly-game.pdf#view=FitH 2021-06-28 15:49:15 1 application/pdf conferencePaper ISBN 978-1-4673-1194-6 978-1-4673-1193-9 978-1-4673-1192-2 2012 IEEE Conference on Computational Intelligence and Games (CIG) DOI 10.1109/CIG.2012.6374168 Granada, Spain IEEE Friberger Marie Gustafsson Togelius Julian Generating interesting Monopoly boards from open data 09/2012 DOI.org (Crossref) http://ieeexplore.ieee.org/document/6374168/ 2021-06-28 15:49:18 288-295 2012 IEEE Conference on Computational Intelligence and Games (CIG) attachment Submitted Version http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=81CA58D9ACCE8CA7412077093E520EFC?doi=10.1.1.348.6099&rep=rep1&type=pdf 2021-06-28 15:49:32 1 application/pdf conferencePaper ISBN 978-1-4244-5770-0 978-1-4244-5771-7 Proceedings of the 2009 Winter Simulation Conference (WSC) DOI 10.1109/WSC.2009.5429349 Austin, TX, USA IEEE Friedman Eric J. Henderson Shane G. Byuen Thomas Gallardo German Gutierrez Estimating the probability that the game of Monopoly never ends 12/2009 DOI.org (Crossref) http://ieeexplore.ieee.org/document/5429349/ 2021-06-28 15:49:23 380-391 2009 Winter Simulation Conference (WSC 2009) attachment Full Text https://moscow.sci-hub.se/3233/bacac19e84c764b72c627d05f55c0ad9/friedman2009.pdf#view=FitH 2021-06-28 15:49:32 1 application/pdf report Learning to Play Monopoly with Monte Carlo Tree Search https://project-archive.inf.ed.ac.uk/ug4/20181042/ug4_proj.pdf conferencePaper ISBN 978-1-72811-895-6 TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) DOI 10.1109/TENCON.2019.8929523 Kochi, India IEEE Arun Edupuganti Rajesh Harikrishna Chakrabarti Debarka Cherala Harikiran George Koshy Monopoly Using Reinforcement Learning 10/2019 DOI.org (Crossref) https://ieeexplore.ieee.org/document/8929523/ 2021-06-28 15:49:50 858-862 TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) attachment Full Text https://sci-hub.se/downloads/2020-04-10/35/arun2019.pdf?rand=60d9ef9f20b26#view=FitH 2021-06-28 15:50:07 1 application/pdf report A Markovian Exploration of Monopoly https://pi4.math.illinois.edu/wp-content/uploads/2014/10/Gartland-Burson-Ferguson-Markovopoly.pdf conferencePaper Learning to play Monopoly: A Reinforcement Learning approach https://intelligence.csd.auth.gr/publication/conference-papers/learning-to-play-monopoly-a-reinforcement-learning-approach/ presentation What’s the Best Monopoly Strategy? https://core.ac.uk/download/pdf/48614184.pdf journalArticle Nakai Kenichiro Takenaga Yasuhiko NP-Completeness of Pandemic 2012 en DOI.org (Crossref) https://www.jstage.jst.go.jp/article/ipsjjip/20/3/20_723/_article 2021-06-28 15:59:47 723-726 20 Journal of Information Processing DOI 10.2197/ipsjjip.20.723 3 Journal of Information Processing ISSN 1882-6652 attachment Full Text https://www.jstage.jst.go.jp/article/ipsjjip/20/3/20_723/_pdf 2021-06-28 15:59:50 1 application/pdf thesis On Solving Pentago http://www.ke.tu-darmstadt.de/lehre/arbeiten/bachelor/2011/Buescher_Niklas.pdf journalArticle arXiv:1906.02330 [cs, stat] Serrino Jack Kleiman-Weiner Max Parkes David C. Tenenbaum Joshua B. Computer Science - Machine Learning Statistics - Machine Learning Computer Science - Multiagent Systems Finding Friend and Foe in Multi-Agent Games Recent breakthroughs in AI for multi-agent games like Go, Poker, and Dota, have seen great strides in recent years. Yet none of these games address the real-life challenge of cooperation in the presence of unknown and uncertain teammates. This challenge is a key game mechanism in hidden role games. Here we develop the DeepRole algorithm, a multi-agent reinforcement learning agent that we test on The Resistance: Avalon, the most popular hidden role game. DeepRole combines counterfactual regret minimization (CFR) with deep value networks trained through self-play. Our algorithm integrates deductive reasoning into vector-form CFR to reason about joint beliefs and deduce partially observable actions. We augment deep value networks with constraints that yield interpretable representations of win probabilities. These innovations enable DeepRole to scale to the full Avalon game. Empirical game-theoretic methods show that DeepRole outperforms other hand-crafted and learned agents in five-player Avalon. DeepRole played with and against human players on the web in hybrid human-agent teams. We find that DeepRole outperforms human players as both a cooperator and a competitor. 2019-06-05 arXiv.org http://arxiv.org/abs/1906.02330 2021-06-28 16:00:28 arXiv: 1906.02330 attachment arXiv Fulltext PDF https://arxiv.org/pdf/1906.02330.pdf 2021-06-28 16:00:35 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/1906.02330 2021-06-28 16:00:38 1 text/html thesis A Mathematical Analysis of the Game of Santorini https://openworks.wooster.edu/independentstudy/8917/ computerProgram A Mathematical Analysis of the Game of Santorini https://github.com/carsongeissler/SantoriniIS report The complexity of Scotland Yard https://eprints.illc.uva.nl/id/eprint/193/1/PP-2006-18.text.pdf conferencePaper ISBN 978-1-4799-3547-5 2014 IEEE Conference on Computational Intelligence and Games DOI 10.1109/CIG.2014.6932907 Dortmund, Germany IEEE Szubert Marcin Jaskowski Wojciech Temporal difference learning of N-tuple networks for the game 2048 8/2014 DOI.org (Crossref) http://ieeexplore.ieee.org/document/6932907/ 2021-06-28 16:09:20 1-8 2014 IEEE Conference on Computational Intelligence and Games (CIG) attachment Submitted Version https://www.cs.put.poznan.pl/mszubert/pub/szubert2014cig.pdf 2021-06-28 16:09:26 1 application/pdf journalArticle arXiv:1501.03837 [cs] Abdelkader Ahmed Acharya Aditya Dasler Philip Computer Science - Computational Complexity F.2.2 On the Complexity of Slide-and-Merge Games We study the complexity of a particular class of board games, which we call `slide and merge' games. Namely, we consider 2048 and Threes, which are among the most popular games of their type. In both games, the player is required to slide all rows or columns of the board in one direction to create a high value tile by merging pairs of equal tiles into one with the sum of their values. This combines features from both block pushing and tile matching puzzles, like Push and Bejeweled, respectively. We define a number of natural decision problems on a suitable generalization of these games and prove NP-hardness for 2048 by reducing from 3SAT. Finally, we discuss the adaptation of our reduction to Threes and conjecture a similar result. 2015-01-15 arXiv.org http://arxiv.org/abs/1501.03837 2021-06-28 16:09:34 arXiv: 1501.03837 attachment arXiv Fulltext PDF https://arxiv.org/pdf/1501.03837.pdf 2021-06-28 16:09:48 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/1501.03837 2021-06-28 16:09:52 1 text/html journalArticle DOI 10.4230/LIPICS.FUN.2016.1 Abdelkader Ahmed Acharya Aditya Dasler Philip Herbstritt Marc 000 Computer science, knowledge, general works Computer Science 2048 Without New Tiles Is Still Hard 2016 en DOI.org (Datacite) http://drops.dagstuhl.de/opus/volltexte/2016/5885/ 2021-06-28 16:09:58 Artwork Size: 14 pages Medium: application/pdf Publisher: Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH, Wadern/Saarbruecken, Germany 14 pages conferencePaper MDA: A Formal Approach to Game Design and Game Research https://aaai.org/Library/Workshops/2004/ws04-04-001.php conferencePaper 6 ISBN 2342-9666 Think Design Play DiGRA/Utrecht School of the Arts Exploring anonymity in cooperative board games This study was done as a part of a larger research project where the interest was on exploring if and how gameplay design could give informative principles to the design of educational activities. The researchers conducted a series of studies trying to map game mechanics that had the special quality of being inclusive, i.e., playable by a diverse group of players. This specific study focused on designing a cooperative board game with the goal of implementing anonymity as a game mechanic. Inspired by the gameplay design patterns methodology (Björk & Holopainen 2005a; 2005b; Holopainen & Björk 2008), mechanics from existing cooperative board games were extracted and analyzed in order to inform the design process. The results from prototyping and play testing indicated that it is possible to implement anonymous actions in cooperative board games and that this mechanic made rather unique forms of gameplay possible. These design patterns can be further developed in order to address inclusive educational practices. January 2011 http://www.digra.org/digital-library/publications/exploring-anonymity-in-cooperative-board-games/ 2011 DiGRA International Conference journalArticle arXiv:2107.07630 [cs] Siu Ho Chit Pena Jaime D. Chang Kimberlee C. Chen Edenna Zhou Yutai Lopez Victor J. Palko Kyle Allen Ross E. Computer Science - Artificial Intelligence Computer Science - Human-Computer Interaction Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi Deep reinforcement learning has generated superhuman AI in competitive games such as Go and StarCraft. Can similar learning techniques create a superior AI teammate for human-machine collaborative games? Will humans prefer AI teammates that improve objective team performance or those that improve subjective metrics of trust? In this study, we perform a single-blind evaluation of teams of humans and AI agents in the cooperative card game Hanabi, with both rule-based and learning-based agents. In addition to the game score, used as an objective metric of the human-AI team performance, we also quantify subjective measures of the human's perceived performance, teamwork, interpretability, trust, and overall preference of AI teammate. We find that humans have a clear preference toward a rule-based AI teammate (SmartBot) over a state-of-the-art learning-based AI teammate (Other-Play) across nearly all subjective metrics, and generally view the learning-based agent negatively, despite no statistical difference in the game score. This result has implications for future AI design and reinforcement learning benchmarking, highlighting the need to incorporate subjective metrics of human-AI teaming rather than a singular focus on objective task performance. 2021-07-19 arXiv.org https://arxiv.org/abs/2107.07630 2021-07-24 06:30:44 arXiv: 2107.07630 attachment 86e8f7ab32cfd12577bc2619bc635690-Paper.pdf https://papers.neurips.cc/paper/2021/file/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf 2022-01-11 07:50:59 3 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2107.07630.pdf 2021-07-24 06:31:01 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2107.07630 2021-07-24 06:31:06 1 text/html journalArticle Litwiller Bonnie H. Duncan David R. Probabilites In Yahtzee Teachers of units in probability are often interested in providing examples of probabilistic situations in a nonclassroom setting. Games are a rich source of such probabilities. Many people enjoy playing a commercial game called Yahtzee. A Yahtzee player receives points for achieving various specified numerical combinations of five dice during the three rolls that constitute a turn. 12/1982 DOI.org (Crossref) https://pubs.nctm.org/view/journals/mt/75/9/article-p751.xml 2021-07-24 07:53:57 751-754 75 The Mathematics Teacher DOI 10.5951/MT.75.9.0751 9 MT ISSN 0025-5769, 2330-0582 presentation Verhoeff Tom Optimal Solitaire Yahtzee Strategies http://www.yahtzee.org.uk/optimal_yahtzee_TV.pdf journalArticle Bonarini Andrea Lazaric Alessandro Restelli Marcello Yahtzee: a Large Stochastic Environment for RL Benchmarks Yahtzee is a game that is regularly played by more than 100 million people in the world. We propose a simplified version of Yahtzee as a benchmark for RL algorithms. We have already used it for this purpose, and an implementation is available. http://researchers.lille.inria.fr/~lazaric/Webpage/PublicationsByTopic_files/bonarini2005yahtzee.pdf 1 thesis KTH, School of Computer Science and Communication (CSC) Serra Andreas Niigata Kai Widell Optimal Yahtzee performance in multi-player games Yahtzee is a game with a moderately large search space, dependent on the factor of luck. This makes it not quite trivial to implement an optimal strategy for it. Using the optimal strategy for single-player use, comparisons against other algorithms are made and the results are analyzed for hints on what it could take to make an algorithm that could beat the single-player optimal strategy. April 12, 2013 en http://www.diva-portal.org/smash/get/diva2:668705/FULLTEXT01.pdf https://www.csc.kth.se/utbildning/kth/kurser/DD143X/dkand13/Group4Per/report/12-serra-widell-nigata.pdf 17 Independent thesis Basic level (degree of Bachelor) manuscript Verhoeff Tom How to Maximize Your Score in Solitaire Yahtzee Yahtzee is a well-known game played with five dice. Players take turns at assembling and scoring dice patterns. The player with the highest score wins. Solitaire Yahtzee is a single-player version of Yahtzee aimed at maximizing one’s score. A strategy for playing Yahtzee determines which choice to make in each situation of the game. We show that the maximum expected score over all Solitaire Yahtzee strategies is 254.5896. . . . en http://www-set.win.tue.nl/~wstomv/misc/yahtzee/yahtzee-report-unfinished.pdf 18 Incomplete Draft thesis Yale University, Department of Computer Science Vasseur Philip Using Deep Q-Learning to Compare Strategy Ladders of Yahtzee “Bots” playing games is not a new concept, likely going back to the first video games. However, there has been a new wave recently using machine learning to learn to play games at a near optimal level - essentially using neural networks to “solve” games. Depending on the game, this can be relatively straight forward using supervised learning. However, this requires having data for optimal play, which is often not possible due to the sheer complexity of many games. For example, solitaire Yahtzee has this data available, but two player Yahtzee does not due to the massive state space. A recent trend in response to this started with Google Deep Mind in 2013, who used Deep Reinforcement Learning to play various Atari games [4]. This project will apply Deep Reinforcement Learning (specifically Deep Q-Learning) and measure how an agent learns to play Yahtzee in the form of a strategy ladder. A strategy ladder is a way of looking at how the performance of an AI varies with the computational resources it uses. Different sets of rules changes how the the AI learns which varies the strategy ladder itself. This project will vary the upper bonus threshold and then attempt to measure how “good” the various strategy ladders are - in essence attempting to find the set of rules which creates the “best” version of Yahtzee. We assume/expect that there is some correlation between strategy ladders for AI and strategy ladders for human, meaning that a game with a “good” strategy ladder for an AI indicates that game is interesting and challenging for humans. December 12, 2019 en https://raw.githubusercontent.com/philvasseur/Yahtzee-DQN-Thesis/dcf2bfe15c3b8c0ff3256f02dd3c0aabdbcbc9bb/webpage/final_report.pdf 12 report KTH Royal Institute Of Technology Computer Science And Communication Defensive Yahtzee In this project an algorithm has been created that plays Yahtzee using rule based heuristics. The focus is getting a high lowest score and a high 10th percentile. All rules of Yahtzee and the probabilities for each combination have been studied and based on this each turn is optimized to get a guaranteed decent high score. The algorithm got a lowest score of 79 and a 10th percentile of 152 when executed 100 000 times. https://www.diva-portal.org/smash/get/diva2:817838/FULLTEXT01.pdf http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168668 22 report Glenn James An Optimal Strategy for Yahtzee http://www.cs.loyola.edu/~jglenn/research/optimal_yahtzee.pdf presentation Middlebury College R. Teal Witter Alex Lyford Applications of Graph Theory and Probability in the Board Game Ticket to Ride January 16, 2020 https://www.rtealwitter.com/slides/2020-JMM.pdf attachment Full Text https://www.rtealwitter.com/slides/2020-JMM.pdf 2021-07-24 08:18:37 1 application/pdf conferencePaper ISBN 978-92-837-2336-3 14th NATO Operations Research and Analysis (OR&A) Conference: Emerging and Disruptive Technology DOI 10.14339/STO-MP-SAS-OCS-ORA-2020-WCM-01-PDF NATO Christoffer Limér Erik Kalmér Mika Cohen Monte Carlo Tree Search for Risk 2/16/2021 en AC/323(SAS-ACT)TP/1017 https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-SAS-OCS-ORA-2020/MP-SAS-OCS-ORA-2020-WCM-01.pdf attachment Full Text https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-SAS-OCS-ORA-2020/MP-SAS-OCS-ORA-2020-WCM-01.pdf 2021-07-24 08:34:15 1 application/pdf presentation Christoffer Limér Erik Kalmér Wargaming with Monte-Carlo Tree Search 2/16/2021 en https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-SAS-OCS-ORA-2020/MP-SAS-OCS-ORA-2020-WCM-01P.pdf attachment Full Text https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-SAS-OCS-ORA-2020/MP-SAS-OCS-ORA-2020-WCM-01P.pdf 2021-07-24 08:35:04 1 application/pdf journalArticle arXiv:1910.04376 [cs] Zha Daochen Lai Kwei-Herng Cao Yuanpu Huang Songyi Wei Ruzhe Guo Junyu Hu Xia Computer Science - Artificial Intelligence RLCard: A Toolkit for Reinforcement Learning in Card Games RLCard is an open-source toolkit for reinforcement learning research in card games. It supports various card environments with easy-to-use interfaces, including Blackjack, Leduc Hold'em, Texas Hold'em, UNO, Dou Dizhu and Mahjong. The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with multiple agents, large state and action space, and sparse reward. In this paper, we provide an overview of the key components in RLCard, a discussion of the design principles, a brief introduction of the interfaces, and comprehensive evaluations of the environments. The codes and documents are available at https://github.com/datamllab/rlcard 2020-02-14 RLCard arXiv.org http://arxiv.org/abs/1910.04376 2021-07-24 08:40:55 arXiv: 1910.04376 attachment arXiv Fulltext PDF https://arxiv.org/pdf/1910.04376.pdf 2021-07-24 08:40:59 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/1910.04376 2021-07-24 08:41:03 1 text/html journalArticle arXiv:2009.12065 [cs] Gaina Raluca D. Balla Martin Dockhorn Alexander Montoliu Raul Perez-Liebana Diego Computer Science - Artificial Intelligence Design and Implementation of TAG: A Tabletop Games Framework This document describes the design and implementation of the Tabletop Games framework (TAG), a Java-based benchmark for developing modern board games for AI research. TAG provides a common skeleton for implementing tabletop games based on a common API for AI agents, a set of components and classes to easily add new games and an import module for defining data in JSON format. At present, this platform includes the implementation of seven different tabletop games that can also be used as an example for further developments. Additionally, TAG also incorporates logging functionality that allows the user to perform a detailed analysis of the game, in terms of action space, branching factor, hidden information, and other measures of interest for Game AI research. The objective of this document is to serve as a central point where the framework can be described at length. TAG can be downloaded at: https://github.com/GAIGResearch/TabletopGames 2020-09-25 Design and Implementation of TAG arXiv.org http://arxiv.org/abs/2009.12065 2021-07-24 08:41:01 arXiv: 2009.12065 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2009.12065.pdf 2021-07-24 08:41:07 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2009.12065 2021-07-24 08:41:11 1 text/html computerProgram Adam Stelmaszczyk Game Tree Search Algorithms - C++ library for AI bot programming. 2015 Game Tree Search Algorithms https://github.com/AdamStelmaszczyk/gtsa C++ computerProgram Raluca D. Gaina Martin Balla Alexander Dockhorn Raul Montoliu Diego Perez-Liebana TAG: Tabletop Games Framework The Tabletop Games Framework (TAG) is a Java-based benchmark for developing modern board games for AI research. TAG provides a common skeleton for implementing tabletop games based on a common API for AI agents, a set of components and classes to easily add new games and an import module for defining data in JSON format. At present, this platform includes the implementation of seven different tabletop games that can also be used as an example for further developments. Additionally, TAG also incorporates logging functionality that allows the user to perform a detailed analysis of the game, in terms of action space, branching factor, hidden information, and other measures of interest for Game AI research. https://github.com/GAIGResearch/TabletopGames MIT License Java journalArticle Osawa Hirotaka Kawagoe Atsushi Sato Eisuke Kato Takuya Emergence of Cooperative Impression With Self-Estimation, Thinking Time, and Concordance of Risk Sensitivity in Playing Hanabi The authors evaluate the extent to which a user’s impression of an AI agent can be improved by giving the agent the ability of self-estimation, thinking time, and coordination of risk tendency. The authors modified the algorithm of an AI agent in the cooperative game Hanabi to have all of these traits, and investigated the change in the user’s impression by playing with the user. The authors used a self-estimation task to evaluate the effect that the ability to read the intention of a user had on an impression. The authors also show thinking time of an agent influences impression for an agent. The authors also investigated the relationship between the concordance of the risk-taking tendencies of players and agents, the player’s impression of agents, and the game experience. The results of the self-estimation task experiment showed that the more accurate the estimation of the agent’s self, the more likely it is that the partner will perceive humanity, affinity, intelligence, and communication skills in the agent. The authors also found that an agent that changes the length of thinking time according to the priority of action gives the impression that it is smarter than an agent with a normal thinking time when the player notices the difference in thinking time or an agent that randomly changes the thinking time. The result of the experiment regarding concordance of the risk-taking tendency shows that influence player’s impression toward agents. These results suggest that game agent designers can improve the player’s disposition toward an agent and the game experience by adjusting the agent’s self-estimation level, thinking time, and risk-taking tendency according to the player’s personality and inner state during the game. 2021-10-12 DOI.org (Crossref) https://www.frontiersin.org/articles/10.3389/frobt.2021.658348/full 2021-11-24 07:14:38 658348 8 Frontiers in Robotics and AI DOI 10.3389/frobt.2021.658348 Front. Robot. AI ISSN 2296-9144 attachment Full Text https://www.frontiersin.org/articles/10.3389/frobt.2021.658348/pdf 2021-11-24 07:15:06 1 application/pdf journalArticle arXiv:2112.03178 [cs] Schmid Martin Moravcik Matej Burch Neil Kadlec Rudolf Davidson Josh Waugh Kevin Bard Nolan Timbers Finbarr Lanctot Marc Holland Zach Davoodi Elnaz Christianson Alden Bowling Michael Computer Science - Artificial Intelligence Computer Science - Machine Learning Computer Science - Computer Science and Game Theory Player of Games Games have a long history of serving as a benchmark for progress in artificial intelligence. Recently, approaches using search and learning have shown strong performance across a set of perfect information games, and approaches using game-theoretic reasoning and learning have shown strong performance for specific imperfect information poker variants. We introduce Player of Games, a general-purpose algorithm that unifies previous approaches, combining guided search, self-play learning, and game-theoretic reasoning. Player of Games is the first algorithm to achieve strong empirical performance in large perfect and imperfect information games -- an important step towards truly general algorithms for arbitrary environments. We prove that Player of Games is sound, converging to perfect play as available computation time and approximation capacity increases. Player of Games reaches strong performance in chess and Go, beats the strongest openly available agent in heads-up no-limit Texas hold'em poker (Slumbot), and defeats the state-of-the-art agent in Scotland Yard, an imperfect information game that illustrates the value of guided search, learning, and game-theoretic reasoning. 2021-12-06 arXiv.org http://arxiv.org/abs/2112.03178 2021-12-12 07:05:28 arXiv: 2112.03178 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2112.03178.pdf 2021-12-12 07:05:42 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2112.03178 2021-12-12 07:05:47 1 text/html journalArticle Silver David Hubert Thomas Schrittwieser Julian Antonoglou Ioannis Lai Matthew Guez Arthur Lanctot Marc Sifre Laurent Kumaran Dharshan Graepel Thore Lillicrap Timothy Simonyan Karen Hassabis Demis A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play 2018-12-07 en DOI.org (Crossref) https://www.science.org/doi/10.1126/science.aar6404 2021-12-12 07:05:58 1140-1144 362 Science DOI 10.1126/science.aar6404 6419 Science ISSN 0036-8075, 1095-9203 attachment Submitted Version https://discovery.ucl.ac.uk/id/eprint/10069050/1/alphazero_preprint.pdf 2021-12-12 07:06:12 1 application/pdf thesis Fort Worth, Texas Texas Christian University Nagel, Lauren Analysis of 'The Settlers of Catan' Using Markov Chains Markov chains are stochastic models characterized by the probability of future states depending solely on one's current state. Google's page ranking system, financial phenomena such as stock market crashes, and algorithms to predict a company's projected sales are a glimpse into the array of applications for Markov models. Board games such as Monopoly and Risk have also been studied under the lens of Markov decision processes. In this research, we analyzed the board game "The Settlers of Catan" using transition matrices. Transition matrices are composed of the current states which represent each row i and the proceeding states across the columns j with the entry (i,j) containing the probability the current state i will transition to the state j. Using these transition matrices, we delved into addressing the question of which starting positions are optimal. Furthermore, we worked on determining optimality in conjunction with a player's gameplay strategy. After building a simulation of the game in python, we tested the results of our theoretical research against the mock run throughs to observe how well our model prevailed under the limitations of time (number of turns before winner is reached). May 3, 2021 en https://repository.tcu.edu/handle/116099117/49062 53 attachment Full Text https://repository.tcu.edu/bitstream/116099117/49062/1/Nagel__Lauren-Honors_Project.pdf 2021-12-19 11:15:58 1 application/pdf attachment Nagel__Lauren-Honors_Project.pdf https://repository.tcu.edu/bitstream/handle/116099117/49062/Nagel__Lauren-Honors_Project.pdf?sequence=1&isAllowed=y 2021-12-19 11:15:50 3 journalArticle arXiv:2009.00655 [cs] Ward Henry N. Brooks Daniel J. Troha Dan Mills Bobby Khakhalin Arseny S. Computer Science - Artificial Intelligence AI solutions for drafting in Magic: the Gathering Drafting in Magic the Gathering is a sub-game within a larger trading card game, where several players progressively build decks by picking cards from a common pool. Drafting poses an interesting problem for game and AI research due to its large search space, mechanical complexity, multiplayer nature, and hidden information. Despite this, drafting remains understudied, in part due to a lack of high-quality, public datasets. To rectify this problem, we present a dataset of over 100,000 simulated, anonymized human drafts collected from Draftsim.com. We also propose four diverse strategies for drafting agents, including a primitive heuristic agent, an expert-tuned complex heuristic agent, a Naive Bayes agent, and a deep neural network agent. We benchmark their ability to emulate human drafting, and show that the deep neural network agent outperforms other agents, while the Naive Bayes and expert-tuned agents outperform simple heuristics. We analyze the accuracy of AI agents across the timeline of a draft, and describe unique strengths and weaknesses for each approach. This work helps to identify next steps in the creation of humanlike drafting agents, and can serve as a benchmark for the next generation of drafting bots. 2021-04-04 AI solutions for drafting in Magic arXiv.org http://arxiv.org/abs/2009.00655 2021-12-19 11:19:03 arXiv: 2009.00655 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2009.00655.pdf 2021-12-19 11:19:09 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2009.00655 2021-12-19 11:19:13 1 text/html journalArticle arXiv:1404.0743 [cs] Irving Geoffrey Computer Science - Distributed, Parallel, and Cluster Computing Pentago is a First Player Win: Strongly Solving a Game Using Parallel In-Core Retrograde Analysis We present a strong solution of the board game pentago, computed using exhaustive parallel retrograde analysis in 4 hours on 98304 ($3 \times 2^{15}$) threads of NERSC's Cray Edison. At $3.0 \times 10^{15}$ states, pentago is the largest divergent game solved to date by two orders of magnitude, and the only example of a nontrivial divergent game solved using retrograde analysis. Unlike previous retrograde analyses, our computation was performed entirely in-core, writing only a small portion of the results to disk; an out-of-core implementation would have been much slower. Symmetry was used to reduce branching factor and exploit instruction level parallelism. Despite a theoretically embarrassingly parallel structure, asynchronous message passing was required to fit the computation into available RAM, causing latency problems on an older Cray machine. All code and data for the project are open source, together with a website which combines database lookup and on-the-fly computation to interactively explore the strong solution. 2014-04-03 Pentago is a First Player Win arXiv.org http://arxiv.org/abs/1404.0743 2021-12-19 11:20:46 arXiv: 1404.0743 attachment arXiv Fulltext PDF https://arxiv.org/pdf/1404.0743.pdf 2021-12-19 11:20:58 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/1404.0743 2021-12-19 11:21:03 1 text/html attachment Source Code https://github.com/girving/pentago 2021-12-19 11:21:48 3 computerProgram A massively parallel pentago solver https://github.com/girving/pentago computerProgram An interactive explorer for perfect pentago play https://perfect-pentago.net/ journalArticle arXiv:1811.00673 [stat] Gilbert Daniel E. Wells Martin T. Statistics - Applications Ludometrics: Luck, and How to Measure It Game theory is the study of tractable games which may be used to model more complex systems. Board games, video games and sports, however, are intractable by design, so "ludological" theories about these games as complex phenomena should be grounded in empiricism. A first "ludometric" concern is the empirical measurement of the amount of luck in various games. We argue against a narrow view of luck which includes only factors outside any player's control, and advocate for a holistic definition of luck as complementary to the variation in effective skill within a population of players. We introduce two metrics for luck in a game for a given population - one information theoretical, and one Bayesian, and discuss the estimation of these metrics using sparse, high-dimensional regression techniques. Finally, we apply these techniques to compare the amount of luck between various professional sports, between Chess and Go, and between two hobby board games: Race for the Galaxy and Seasons. 2018-11-01 Ludometrics arXiv.org http://arxiv.org/abs/1811.00673 2021-12-19 11:25:28 arXiv: 1811.00673 attachment arXiv Fulltext PDF https://arxiv.org/pdf/1811.00673.pdf 2021-12-19 11:25:31 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/1811.00673 2021-12-19 11:25:35 1 text/html journalArticle arXiv:2102.10540 [cs] Perez Luis Computer Science - Artificial Intelligence Computer Science - Multiagent Systems Computer Science - Computer Science and Game Theory Mastering Terra Mystica: Applying Self-Play to Multi-agent Cooperative Board Games In this paper, we explore and compare multiple algorithms for solving the complex strategy game of Terra Mystica, hereafter abbreviated as TM. Previous work in the area of super-human game-play using AI has proven effective, with recent break-through for generic algorithms in games such as Go, Chess, and Shogi \cite{AlphaZero}. We directly apply these breakthroughs to a novel state-representation of TM with the goal of creating an AI that will rival human players. Specifically, we present the initial results of applying AlphaZero to this state-representation and analyze the strategies developed. A brief analysis is presented. We call this modified algorithm with our novel state-representation AlphaTM. In the end, we discuss the success and shortcomings of this method by comparing against multiple baselines and typical human scores. All code used for this paper is available at on \href{https://github.com/kandluis/terrazero}{GitHub}. 2021-02-21 Mastering Terra Mystica arXiv.org http://arxiv.org/abs/2102.10540 2021-12-19 11:25:55 arXiv: 2102.10540 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2102.10540.pdf 2021-12-19 11:26:10 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2102.10540 2021-12-19 11:26:14 1 text/html attachment Dataset https://www.kaggle.com/lemonkoala/terra-mystica 2021-12-19 11:27:41 3 attachment Source Code https://github.com/kandluis/terrazero 2021-12-19 11:29:03 3 computerProgram TM AI: Play TM against AI players. https://lodev.org/tmai/ journalArticle arXiv:1710.05121 [cs] Bosboom Jeffrey Hoffmann Michael Computer Science - Computational Complexity F.1.3 Netrunner Mate-in-1 or -2 is Weakly NP-Hard We prove that deciding whether the Runner can win this turn (mate-in-1) in the Netrunner card game generalized to allow decks to contain an arbitrary number of copies of a card is weakly NP-hard. We also prove that deciding whether the Corp can win within two turns (mate-in-2) in this generalized Netrunner is weakly NP-hard. 2017-10-13 arXiv.org http://arxiv.org/abs/1710.05121 2021-12-19 11:33:02 arXiv: 1710.05121 attachment arXiv Fulltext PDF https://arxiv.org/pdf/1710.05121.pdf 2021-12-19 11:33:05 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/1710.05121 2021-12-19 11:33:09 1 text/html journalArticle arXiv:1904.10656 [cs] Fontaine Matthew C. Lee Scott Soros L. B. Silva Fernando De Mesentier Togelius Julian Hoover Amy K. Computer Science - Neural and Evolutionary Computing Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries Quality diversity (QD) algorithms such as MAP-Elites have emerged as a powerful alternative to traditional single-objective optimization methods. They were initially applied to evolutionary robotics problems such as locomotion and maze navigation, but have yet to see widespread application. We argue that these algorithms are perfectly suited to the rich domain of video games, which contains many relevant problems with a multitude of successful strategies and often also multiple dimensions along which solutions can vary. This paper introduces a novel modification of the MAP-Elites algorithm called MAP-Elites with Sliding Boundaries (MESB) and applies it to the design and rebalancing of Hearthstone, a popular collectible card game chosen for its number of multidimensional behavior features relevant to particular styles of play. To avoid overpopulating cells with conflated behaviors, MESB slides the boundaries of cells based on the distribution of evolved individuals. Experiments in this paper demonstrate the performance of MESB in Hearthstone. Results suggest MESB finds diverse ways of playing the game well along the selected behavioral dimensions. Further analysis of the evolved strategies reveals common patterns that recur across behavioral dimensions and explores how MESB can help rebalance the game. 2019-04-24 arXiv.org http://arxiv.org/abs/1904.10656 2021-12-19 11:33:35 arXiv: 1904.10656 attachment arXiv Fulltext PDF https://arxiv.org/pdf/1904.10656.pdf 2021-12-19 11:33:53 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/1904.10656 2021-12-19 11:33:57 1 text/html journalArticle arXiv:2112.09697 [cs] Galván Edgar Simpson Gavin Computer Science - Artificial Intelligence Computer Science - Machine Learning Computer Science - Neural and Evolutionary Computing On the Evolution of the MCTS Upper Confidence Bounds for Trees by Means of Evolutionary Algorithms in the Game of Carcassonne Monte Carlo Tree Search (MCTS) is a sampling best-first method to search for optimal decisions. The MCTS's popularity is based on its extraordinary results in the challenging two-player based game Go, a game considered much harder than Chess and that until very recently was considered infeasible for Artificial Intelligence methods. The success of MCTS depends heavily on how the tree is built and the selection process plays a fundamental role in this. One particular selection mechanism that has proved to be reliable is based on the Upper Confidence Bounds for Trees, commonly referred as UCT. The UCT attempts to nicely balance exploration and exploitation by considering the values stored in the statistical tree of the MCTS. However, some tuning of the MCTS UCT is necessary for this to work well. In this work, we use Evolutionary Algorithms (EAs) to evolve mathematical expressions with the goal to substitute the UCT mathematical expression. We compare our proposed approach, called Evolution Strategy in MCTS (ES-MCTS) against five variants of the MCTS UCT, three variants of the star-minimax family of algorithms as well as a random controller in the Game of Carcassonne. We also use a variant of our proposed EA-based controller, dubbed ES partially integrated in MCTS. We show how the ES-MCTS controller, is able to outperform all these 10 intelligent controllers, including robust MCTS UCT controllers. 2021-12-17 arXiv.org http://arxiv.org/abs/2112.09697 2021-12-25 07:03:23 arXiv: 2112.09697 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2112.09697.pdf 2021-12-25 07:03:26 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2112.09697 2021-12-25 07:03:32 1 text/html conferencePaper Brandon Cui Hengyuan Hu Luis Pineda Jakob Foerster K-level Reasoning for Zero-Shot Coordination in Hanabi The standard problem setting in cooperative multi-agent settings is \emph{self-play} (SP), where the goal is to train a \emph{team} of agents that works well together. However, optimal SP policies commonly contain arbitrary conventions (``handshakes'') and are not compatible with other, independently trained agents or humans. This latter desiderata was recently formalized by \cite{Hu2020-OtherPlay} as the \emph{zero-shot coordination} (ZSC) setting and partially addressed with their \emph{Other-Play} (OP) algorithm, which showed improved ZSC and human-AI performance in the card game Hanabi. OP assumes access to the symmetries of the environment and prevents agents from breaking these in a mutually \emph{incompatible} way during training. However, as the authors point out, discovering symmetries for a given environment is a computationally hard problem. Instead, we show that through a simple adaption of k-level reasoning (KLR) \cite{Costa-Gomes2006-K-level}, synchronously training all levels, we can obtain competitive ZSC and ad-hoc teamplay performance in Hanabi, including when paired with a human-like proxy bot. We also introduce a new method, synchronous-k-level reasoning with a best response (SyKLRBR), which further improves performance on our synchronous KLR by co-training a best response. https://papers.neurips.cc/paper/2021/hash/4547dff5fd7604f18c8ee32cf3da41d7-Abstract.html Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021) attachment Paper https://papers.neurips.cc/paper/2021/file/4547dff5fd7604f18c8ee32cf3da41d7-Paper.pdf 2022-01-11 07:52:40 3 attachment Supplemental https://papers.neurips.cc/paper/2021/file/4547dff5fd7604f18c8ee32cf3da41d7-Supplemental.pdf 2022-01-11 07:52:49 3 journalArticle Ford Cassandra Ohata Merrick Game Balancing in Dominion: An Approach to Identifying Problematic Game Elements In the popular card game Dominion, the configuration of game elements greatly affects the experience for players. If one were redesigning Dominion, therefore, it may be useful to identify game elements that reduce the number of viable strategies in any given game configuration - i.e. elements that are unbalanced. In this paper, we propose an approach that assigns credit to the outcome of an episode to individual elements. Our approach uses statistical analysis to learn the interactions and dependencies between game elements. This learned knowledge is used to recommend elements to game designers for further consideration. Designers may then choose to modify the recommended elements with the goal of increasing the number of viable strategies. en https://web.archive.org/web/20220516093249/http://cs.gettysburg.edu/~tneller/games/aiagd/papers/EAAI-00039-FordC.pdf Zotero http://cs.gettysburg.edu/~tneller/games/aiagd/papers/EAAI-00039-FordC.pdf 7 attachment Ford and Ohata - Game Balancing in Dominion An Approach to Identif.pdf http://cs.gettysburg.edu/~tneller/games/aiagd/papers/EAAI-00039-FordC.pdf 2022-03-12 09:44:51 1 application/pdf journalArticle arXiv:2203.11656 [cs] Grooten Bram Wemmenhove Jelle Poot Maurice Portegies Jim Computer Science - Artificial Intelligence Computer Science - Machine Learning Computer Science - Multiagent Systems Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for Hanabi In pursuit of enhanced multi-agent collaboration, we analyze several on-policy deep reinforcement learning algorithms in the recently published Hanabi benchmark. Our research suggests a perhaps counter-intuitive finding, where Proximal Policy Optimization (PPO) is outperformed by Vanilla Policy Gradient over multiple random seeds in a simplified environment of the multi-agent cooperative card game. In our analysis of this behavior we look into Hanabi-specific metrics and hypothesize a reason for PPO's plateau. In addition, we provide proofs for the maximum length of a perfect game (71 turns) and any game (89 turns). Our code can be found at: https://github.com/bramgrooten/DeepRL-for-Hanabi 2022-03-22 Is Vanilla Policy Gradient Overlooked? arXiv.org http://arxiv.org/abs/2203.11656 2022-03-26 04:22:52 arXiv: 2203.11656 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2203.11656.pdf 2022-03-26 04:24:09 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2203.11656 2022-03-26 04:24:17 1 text/html attachment Full Text https://arxiv.org/pdf/2203.11656.pdf 2022-03-26 04:24:21 1 application/pdf blogPost Henry Charlesworth Learning to Play Settlers of Catan with Deep Reinforcement Learning https://settlers-rl.github.io/ journalArticle Nguyen Viet-Ha Perrot Kévin Rikudo is NP-complete 04/2022 en DOI.org (Crossref) https://linkinghub.elsevier.com/retrieve/pii/S0304397522000457 2022-04-19 07:12:59 34-47 910 Theoretical Computer Science DOI 10.1016/j.tcs.2022.01.034 Theoretical Computer Science ISSN 03043975 conferencePaper ISBN 978-1-4503-9143-6 Proceedings of the 2022 ACM/SPEC on International Conference on Performance Engineering DOI 10.1145/3489525.3511685 Beijing China ACM de Goede Danilo Kampert Duncan Varbanescu Ana Lucia The Cost of Reinforcement Learning for Game Engines: The AZ-Hive Case-study 2022-04-09 en The Cost of Reinforcement Learning for Game Engines DOI.org (Crossref) https://dl.acm.org/doi/10.1145/3489525.3511685 2022-04-19 07:14:12 145-152 ICPE '22: ACM/SPEC International Conference on Performance Engineering attachment Full Text https://dl.acm.org/doi/pdf/10.1145/3489525.3511685 2022-04-19 07:14:15 1 application/pdf journalArticle IEEE Transactions on Games DOI 10.1109/TG.2022.3169168 IEEE Trans. Games ISSN 2475-1502, 2475-1510 Canaan Rodrigo Gao Xianbo Togelius Julian Nealen Andy Menzel Stefan Generating and Adapting to Diverse Ad-Hoc Partners in Hanabi 2022 DOI.org (Crossref) https://ieeexplore.ieee.org/document/9762901/ 2022-04-30 05:11:33 1-1 thesis Rijksuniversiteit Groningen Nicholas Kees Dupuis Theory of Mind for Multi-agent Coordination in Hanabi In order to successfully coordinate in complex multi-agent environments, AI systems need the ability to build useful models of others. Building such models often benefits from the use of theory of mind, by representing unobservable mental states of another agent, including their desires, beliefs, and intentions. In this paper I will show how theory of mind affects the ability of agents to coordinate in the cooperative card game Hanabi. The ability to play Hanabi well with a wide range of partners requires reasoning about the beliefs and intentions of other players, which makes Hanabi a perfect testbed for studying theory of mind. I will use both symbolic agent-based models designed to play a simplified version of the game which explicitly engage in theory of mind as well as reinforcement learning agents which use meta-learning to play the full version of the game. Both methods were used to build models of other agents and thereby test how theory of mind can both promote coordination as well as lead to coordination failure. My research demonstrates that the effect of theory of mind is highly variable, and depends heavily on the type of theory of mind reasoning being done by the partner. The empirical results of the agent-based models suggest that theory of mind is best applied when the joint policy produced without theory of mind is far from optimal, in which case second-order theory of mind appears to offer the most significant advantage. 16 Aug 2022 en-US http://fse.studenttheses.ub.rug.nl/id/eprint/28327 63 Thesis (Master's Thesis / Essay) attachment Full Text PDF https://fse.studenttheses.ub.rug.nl/28327/1/mAI_2022_DupuisNK.pdf 2022-08-23 11:54:35 3 thesis Örebro University, School of Science and Technology. Inferadi, Salam Olof, Johnsson The Hanabi challenge: From Artificial Teams to Mixed Human-Machine Teams This report will describe the further development of the Graphical User Interface (GUI) for the Hanabi Benchmark. Hanabi is a card game that has been introduced as a new frontier for artificial intelligence (AI). The goal of the project was to implement a human-user, into the GUI, and give the possibility to play against Machine Learning (ML) based agents, viz, non-human players in the GUI.To achieve these goals, we implemented human controls into the GUI to give a human user the option to play the game in the GUI. Agent models were integrated into to the GUI for the human to play with. Finally, a small study was conducted to evaluate the agent’s performances. en http://oru.diva-portal.org/smash/record.jsf?pid=diva2%3A1691114&dswid=-1981 40 Independent thesis Basic level (degree of Bachelor) attachment Fulltext PDF https://www.diva-portal.org/smash/get/diva2:1691114/FULLTEXT01.pdf 2022-09-07 10:06:48 3 thesis Örebro University, School of Science and Technology Nguyen, Van Hoa A Graphical User Interface For The Hanabi Challenge Benchmark This report will describe the development of the Graphical User Interface (GUI) forthe Hanabi Challenge Benchmark. The benchmark is based on the popular cardgame Hanabi and presents itself as a new research frontier in artificial intelligencefor cooperative multi-agent challenges. The project’s intentions and goals are tointerpret and visualize the data output from the benchmark to give us a better understandingof it.A GUI was then developed by using knowledge within theory of mind in combinationwith theories within human-computer interaction. The results of this project wereevaluated through a small-scale usability test. Users of different ages, gender andlevels of computer knowledge tested the application and through a questionnaire,the quality of the GUI was assessed. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-94615 33 Independent thesis Basic level (professional degree) attachment PDF https://www.diva-portal.org/smash/get/diva2:1597503/FULLTEXT01.pdf 2022-09-07 10:09:15 3 preprint arXiv Galván Edgar Simpson Gavin Ameneyro Fred Valdez Computer Science - Neural and Evolutionary Computing Evolving the MCTS Upper Confidence Bounds for Trees Using a Semantic-inspired Evolutionary Algorithm in the Game of Carcassonne Monte Carlo Tree Search (MCTS) is a sampling best-first method to search for optimal decisions. The success of MCTS depends heavily on how the tree is built and the selection process plays a fundamental role in this. One particular selection mechanism that has proved to be reliable is based on the Upper Confidence Bounds for Trees (UCT). The UCT attempts to balance exploration and exploitation by considering the values stored in the statistical tree of the MCTS. However, some tuning of the MCTS UCT is necessary for this to work well. In this work, we use Evolutionary Algorithms (EAs) to evolve mathematical expressions with the goal to substitute the UCT formula and use the evolved expressions in MCTS. More specifically, we evolve expressions by means of our proposed Semantic-inspired Evolutionary Algorithm in MCTS approach (SIEA-MCTS). This is inspired by semantics in Genetic Programming (GP), where the use of fitness cases is seen as a requirement to be adopted in GP. Fitness cases are normally used to determine the fitness of individuals and can be used to compute the semantic similarity (or dissimilarity) of individuals. However, fitness cases are not available in MCTS. We extend this notion by using multiple reward values from MCTS that allow us to determine both the fitness of an individual and its semantics. By doing so, we show how SIEA-MCTS is able to successfully evolve mathematical expressions that yield better or competitive results compared to UCT without the need of tuning these evolved expressions. We compare the performance of the proposed SIEA-MCTS against MCTS algorithms, MCTS Rapid Action Value Estimation algorithms, three variants of the *-minimax family of algorithms, a random controller and two more EA approaches. We consistently show how SIEA-MCTS outperforms most of these intelligent controllers in the challenging game of Carcassonne. 2022-08-29 arXiv.org http://arxiv.org/abs/2208.13589 2022-10-09 09:56:30 arXiv:2208.13589 [cs] arXiv:2208.13589 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2208.13589.pdf 2022-10-09 09:56:41 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2208.13589 2022-10-09 09:56:47 1 text/html blogPost William Zhang Fan Pu Zeng Analysis of Symmetry and Conventions in Off-Belief Learning (OBL) in Hanabi We investigate if policies learnt by agents using the Off-Belief Learning (OBL) algorithm in the multi-player cooperative game Hanabi in the zero-shot coordination (ZSC) context are invariant across symmetries of the game, and if any conventions formed during training are arbitrary or natural. We do this by a convention analysis on the action matrix of what the agent does, introduce a novel technique called the Intervention Analysis to estimate if the actions taken by the policies learnt are equivalent between isomorphisms of the same game state, and finally evaluate if our observed results also hold in a simplified version of Hanabi which we call Mini-Hanabi. https://fanpu.io/blog/2022/symmetry-and-conventions-in-obl-hanabi/ attachment Analysis_Of_Symmetry_And_Conventions_In_Off_Belief_Learning_In_Hanabi.pdf https://fanpu.io/assets/research/Analysis_Of_Symmetry_And_Conventions_In_Off_Belief_Learning_In_Hanabi.pdf 2023-03-01 11:40:51 3 thesis Computer Science Department School of Computer Science, Carnegie Mellon University Arnav Mahajan Using intuitive behavior models to adapt to and work with human teammates in Hanabi An agent that can rapidly and accurately model its teammate is a powerful tool in the field of Collaborative AI. Furthermore, if an approximation for this goal was possible in the field of Human-AI Collaboration, teams of people and machines could be more efficient and effective immediately after starting to work together. Using the cooperative card game Hanabi as a testbed, we developed the Chief agent, which models teammates using a pool of intuitive behavioral models. To achieve the goal of rapid learning, it uses Bayesian inference to quickly evaluate the different models relative to each other. To generate an accurate model, it uses historical data augmented by up-to-date knowledge and sampling methods to handle environmental noise and unknowns. We demonstrate that the Chief's mechanisms for modeling and understanding the teammate show promise, but the overall performance still can use improvement to reliably outperform a solution which skips inferring a best strategy and assumes all strategies in the pool are equally likely for the teammate. en http://reports-archive.adm.cs.cmu.edu/anon/anon/usr0/ftp/usr/ftp/2022/abstracts/22-119.html 43 M.S. Thesis attachment CMU-CS-22-119.pdf http://reports-archive.adm.cs.cmu.edu/anon/2022/CMU-CS-22-119.pdf 2023-03-11 03:49:56 3 preprint arXiv Jeon Hyeonchang Kim Kyung-Joong Computer Science - Artificial Intelligence Behavioral Differences is the Key of Ad-hoc Team Cooperation in Multiplayer Games Hanabi Ad-hoc team cooperation is the problem of cooperating with other players that have not been seen in the learning process. Recently, this problem has been considered in the context of Hanabi, which requires cooperation without explicit communication with the other players. While in self-play strategies cooperating on reinforcement learning (RL) process has shown success, there is the problem of failing to cooperate with other unseen agents after the initial learning is completed. In this paper, we categorize the results of ad-hoc team cooperation into Failure, Success, and Synergy and analyze the associated failures. First, we confirm that agents learning via RL converge to one strategy each, but not necessarily the same strategy and that these agents can deploy different strategies even though they utilize the same hyperparameters. Second, we confirm that the larger the behavioral difference, the more pronounced the failure of ad-hoc team cooperation, as demonstrated using hierarchical clustering and Pearson correlation. We confirm that such agents are grouped into distinctly different groups through hierarchical clustering, such that the correlation between behavioral differences and ad-hoc team performance is -0.978. Our results improve understanding of key factors to form successful ad-hoc team cooperation in multi-player games. 2023-03-12 arXiv.org http://arxiv.org/abs/2303.06775 2023-03-16 06:56:29 arXiv:2303.06775 [cs] arXiv:2303.06775 attachment 2303.06775.pdf https://arxiv.org/pdf/2303.06775.pdf 2023-03-16 06:56:59 3 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2303.06775.pdf 2023-03-16 06:57:01 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2303.06775 2023-03-16 06:57:08 1 text/html thesis Technical University of Crete, Greece School of Electronic and Computer Engineering Sofia Maria Nikolakaki Algorithm Modeling for Hardware Implementation of a Blokus Duo Player we applied the algorithm on the game of Blokus Duo, a relatively new game, open for research since it meets the requirements of MCTS and appears to be demanding. More specifically, we present four competitive Blokus Duo players and show that the ones, based on Monte Carlo simulations outperform the Minimax-based one. To the best of our knowledge, this is the first work that compares for the same game, software-based Minimax, Monte Carlo and MCTS players. For each of these players we suggest opportunities for hard- ware implementation and discuss potential bottlenecks. Furthermore, we apply certain heuristics on our MCTS-based player to understand how they affect the efficiency of the algorithm specifically for the game of Blokus Duo. http://artemis.library.tuc.gr/DT2014-0060/DT2014-0060.pdf 109 attachment DT2014-0060.pdf http://artemis.library.tuc.gr/DT2014-0060/DT2014-0060.pdf 2023-04-17 05:47:30 3 journalArticle 6 Journal Information System Development (ISD) 2 Hong Liang Cai Sebastian Aldi Winston Renatan Artificial Intelligence for Blokus Classic using Heuristics, FloodFill, and Greedy Algorithm Blokus is an abstract strategy game which has complex variable to determine ones move. We propose an idea for Artificial Intelligence in Blokus using Heuristics, FloodFill, and Greedy algorithm, which will be called as LeakyAI. Both the game and its implementation are established in Java language. To test the result, we performed benchmarking towards Brute Force AI, itself, and the developers. 2 July 2021 Indonesian https://ejournal-medan.uph.edu/index.php/ISD/article/view/433 9 attachment 263 https://ejournal-medan.uph.edu/index.php/isd/article/view/433/263 2023-04-17 05:55:48 3 conferencePaper ISBN 978-1-4799-6245-7 2014 International Conference on Field-Programmable Technology (FPT) DOI 10.1109/FPT.2014.7082825 Shanghai, China IEEE Kojima Akira FPGA implementation of Blokus Duo player using hardware/software co-design 12/2014 DOI.org (Crossref) http://ieeexplore.ieee.org/document/7082825/ 2023-04-17 05:56:43 378-381 2014 International Conference on Field-Programmable Technology (FPT) journalArticle Austin Jathan Curl Emelie Exploring Combinatorics and Graph Theory with Simple Blokus 2022-08-08 en DOI.org (Crossref) https://www.tandfonline.com/doi/full/10.1080/07468342.2022.2100147 2023-04-17 05:57:05 273-281 53 The College Mathematics Journal DOI 10.1080/07468342.2022.2100147 4 The College Mathematics Journal ISSN 0746-8342, 1931-1346 conferencePaper ISBN 978-1-4799-2198-0 978-1-4799-2199-7 2013 International Conference on Field-Programmable Technology (FPT) DOI 10.1109/FPT.2013.6718428 Kyoto, Japan IEEE Olivito Javier Gonzalez Carlos Resano Javier An FPGA-based specific processor for Blokus Duo 12/2013 DOI.org (Crossref) http://ieeexplore.ieee.org/document/6718428/ 2023-04-17 05:57:28 502-505 2013 International Conference on Field-Programmable Technology (FPT) report San Luis Obispo California Polytechnic State University Chin Chao Blokus Game Solver Blokus (officially pronounced as “Block us”) is an abstract strategy board game with transparent Tetris-shaped, color pieces that players are trying to place onto the board. However, the players can only place a piece that touches at least one corner of their own pieces on the board. The ultimate goal of the game is to place as many pieces onto the board as a player can while blocking off the opponent’s ability to place more pieces onto the board. Each player has pieces with different shapes and sizes that can be placed onto the board, where each block within a piece counts as one point. The player that scores the highest wins the game. Just like other strategy board game such as chess, Blokus contains definite strategic patterns that can be solved with computer algorithms. Various algorithms were discovered and created to develop winning strategy and AI against human opponents. In this work, I am developing random and different greedy strategies to analyze the effectiveness of different factors such as pieces’ size, corner availability, and first-player turn. December 2018 https://digitalcommons.calpoly.edu/cpesp/290/ attachment viewcontent.cgi https://digitalcommons.calpoly.edu/cgi/viewcontent.cgi?article=1305&context=cpesp 2023-04-17 05:59:48 3 conferencePaper ISBN 978-1-4799-6245-7 2014 International Conference on Field-Programmable Technology (FPT) DOI 10.1109/FPT.2014.7082823 Shanghai, China IEEE Qasemi Ehsan Samadi Amir Shadmehr Mohammad H. Azizian Bardia Mozaffari Sajjad Shirian Amir Alizadeh Bijan Highly scalable, shared-memory, Monte-Carlo tree search based Blokus Duo Solver on FPGA 12/2014 DOI.org (Crossref) http://ieeexplore.ieee.org/document/7082823/ 2023-04-17 06:00:24 370-373 2014 International Conference on Field-Programmable Technology (FPT) attachment Full Text https://moscow.sci-hub.se/4176/0772802f9865a79a36cf1a5c77267101/qasemi2014.pdf#navpanes=0&view=FitH 2023-04-17 06:00:31 1 application/pdf conferencePaper ISBN 978-1-4799-2198-0 978-1-4799-2199-7 2013 International Conference on Field-Programmable Technology (FPT) DOI 10.1109/FPT.2013.6718423 Kyoto, Japan IEEE Liu Chester Implementation of a highly scalable blokus duo solver on FPGA 12/2013 DOI.org (Crossref) http://ieeexplore.ieee.org/document/6718423/ 2023-04-17 06:00:52 482-485 2013 International Conference on Field-Programmable Technology (FPT) conferencePaper ISBN 978-1-4799-2198-0 978-1-4799-2199-7 2013 International Conference on Field-Programmable Technology (FPT) DOI 10.1109/FPT.2013.6718429 Kyoto, Japan IEEE Kojima Akira An implementation of Blokus Duo player on FPGA 12/2013 DOI.org (Crossref) http://ieeexplore.ieee.org/document/6718429/ 2023-04-17 06:01:05 506-509 2013 International Conference on Field-Programmable Technology (FPT) conferencePaper ISBN 978-1-4799-2198-0 978-1-4799-2199-7 2013 International Conference on Field-Programmable Technology (FPT) DOI 10.1109/FPT.2013.6718425 Kyoto, Japan IEEE Altman Erik Auerbach Joshua S. Bacon David F. Baldini Ioana Cheng Perry Fink Stephen J. Rabbah Rodric M. The Liquid Metal Blokus Duo Design 12/2013 DOI.org (Crossref) http://ieeexplore.ieee.org/document/6718425/ 2023-04-17 06:01:22 490-493 2013 International Conference on Field-Programmable Technology (FPT) conferencePaper ISBN 978-1-4799-2198-0 978-1-4799-2199-7 2013 International Conference on Field-Programmable Technology (FPT) DOI 10.1109/FPT.2013.6718427 Kyoto, Japan IEEE Sugimoto Naru Miyajima Takaaki Kuhara Takuya Katuta Yuki Mitsuichi Takushi Amano Hideharu Artificial intelligence of Blokus Duo on FPGA using Cyber Work Bench 12/2013 DOI.org (Crossref) http://ieeexplore.ieee.org/document/6718427/ 2023-04-17 06:03:04 498-501 2013 International Conference on Field-Programmable Technology (FPT) conferencePaper ISBN 978-1-4799-6245-7 2014 International Conference on Field-Programmable Technology (FPT) DOI 10.1109/FPT.2014.7082820 Shanghai, China IEEE Sugimoto Naru Amano Hideharu Hardware/software co-design architecture for Blokus Duo solver 12/2014 DOI.org (Crossref) http://ieeexplore.ieee.org/document/7082820/ 2023-04-17 06:03:28 358-361 2014 International Conference on Field-Programmable Technology (FPT) conferencePaper ISBN 978-1-4799-2198-0 978-1-4799-2199-7 2013 International Conference on Field-Programmable Technology (FPT) DOI 10.1109/FPT.2013.6718424 Kyoto, Japan IEEE Cai Jiu Cheng Lian Ruolong Wang Mengyao Canis Andrew Choi Jongsok Fort Blair Hart Eric Miao Emily Zhang Yanyan Calagar Nazanin Brown Stephen Anderson Jason From C to Blokus Duo with LegUp high-level synthesis 12/2013 DOI.org (Crossref) http://ieeexplore.ieee.org/document/6718424/ 2023-04-17 06:03:46 486-489 2013 International Conference on Field-Programmable Technology (FPT) conferencePaper ISBN 978-1-4799-6245-7 2014 International Conference on Field-Programmable Technology (FPT) DOI 10.1109/FPT.2014.7082824 Shanghai, China IEEE Mashimo Susumu Fukuda Kansuke Amagasaki Motoki Iida Masahiro Kuga Morihiro Sueyoshi Toshinori Blokus Duo engine on a Zynq 12/2014 DOI.org (Crossref) http://ieeexplore.ieee.org/document/7082824/ 2023-04-17 06:04:11 374-377 2014 International Conference on Field-Programmable Technology (FPT) conferencePaper ISBN 978-1-4799-6245-7 2014 International Conference on Field-Programmable Technology (FPT) DOI 10.1109/FPT.2014.7082821 Shanghai, China IEEE Borhanifar Hossein Zolnouri Seyed Peyman Optimize MinMax algorithm to solve Blokus Duo game by HDL 12/2014 DOI.org (Crossref) http://ieeexplore.ieee.org/document/7082821/ 2023-04-17 06:05:55 362-365 2014 International Conference on Field-Programmable Technology (FPT) journalArticle Ando Yuki Ogawa Masataka Mizoguchi Yuya Kumagai Kouta Torng-Der Miaw Honda Shinya A Case Study of FPGA Blokus Duo Solver by System-Level Design This paper presents a case study to design a Blokus Duo solver by using our system-level design toolkitnamed SystemBuilder. We start with a modeling of the Blokus nDuo solver by C language and communication APIs which are provided by SystemBuilder. Then, we iteratively verified and tuned the parameters in the solver by running the model on a general computer in order to improve the performance of the solver. Finally, the implementation on FPGA was automatically generated from the model by SystemBuilder. Despite the FPGA implementation, we have never written hardware description language throughout the case study. The case study demonstrates the easiness to design system on FPGA by System-level design tools. 2014-12-03 en DOI.org (Crossref) https://dl.acm.org/doi/10.1145/2693714.2693725 2023-04-17 06:06:14 57-62 42 ACM SIGARCH Computer Architecture News DOI 10.1145/2693714.2693725 4 SIGARCH Comput. Archit. News ISSN 0163-5964 journalArticle Marchionna Luca Pugliese Giulio Martini Mauro Angarano Simone Salvetti Francesco Chiaberge Marcello Deep Instance Segmentation and Visual Servoing to Play Jenga with a Cost-Effective Robotic System The game of Jenga is a benchmark used for developing innovative manipulation solutions for complex tasks. Indeed, it encourages the study of novel robotics methods to successfully extract blocks from a tower. A Jenga game involves many traits of complex industrial and surgical manipulation tasks, requiring a multi-step strategy, the combination of visual and tactile data, and the highly precise motion of a robotic arm to perform a single block extraction. In this work, we propose a novel, cost-effective architecture for playing Jenga with e.Do, a 6DOF anthropomorphic manipulator manufactured by Comau, a standard depth camera, and an inexpensive monodirectional force sensor. Our solution focuses on a visual-based control strategy to accurately align the end-effector with the desired block, enabling block extraction by pushing. To this aim, we trained an instance segmentation deep learning model on a synthetic custom dataset to segment each piece of the Jenga tower, allowing for visual tracking of the desired block’s pose during the motion of the manipulator. We integrated the visual-based strategy with a 1D force sensor to detect whether the block could be safely removed by identifying a force threshold value. Our experimentation shows that our low-cost solution allows e.DO to precisely reach removable blocks and perform up to 14 consecutive extractions in a row. 2023-01-09 en DOI.org (Crossref) https://www.mdpi.com/1424-8220/23/2/752 2023-04-17 06:12:40 752 23 Sensors DOI 10.3390/s23020752 2 Sensors ISSN 1424-8220 attachment Full Text https://www.mdpi.com/1424-8220/23/2/752/pdf?version=1673261970 2023-04-17 06:12:46 1 application/pdf journalArticle Kelly Kathryn Liese Jeffrey Let’s Get Rolling! Exact Optimal Solitaire Yahtzee 2022-05-27 en DOI.org (Crossref) https://www.tandfonline.com/doi/full/10.1080/0025570X.2022.2055334 2023-04-17 06:14:24 205-219 95 Mathematics Magazine DOI 10.1080/0025570X.2022.2055334 3 Mathematics Magazine ISSN 0025-570X, 1930-0980 conferencePaper ISBN 978-1-4503-8807-8 International Conference on the Foundations of Digital Games DOI 10.1145/3402942.3409778 Bugibba Malta ACM Saraiva Rommel Dias Grichshenko Alexandr Araújo Luiz Jonatã Pires de Amaro Junior Bonfim de Carvalho Guilherme Nepomuceno Using Ant Colony Optimisation for map generation and improving game balance in the Terra Mystica and Settlers of Catan board games 2020-09-15 en DOI.org (Crossref) https://dl.acm.org/doi/10.1145/3402942.3409778 2023-04-17 06:15:08 1-7 FDG '20: International Conference on the Foundations of Digital Games bookSection 12145 ISBN 978-3-030-53955-9 978-3-030-53956-6 Advances in Swarm Intelligence Cham Springer International Publishing Tan Ying Shi Yuhui Tuba Milan de Araújo Luiz Jonatã Pires Grichshenko Alexandr Pinheiro Rodrigo Lankaites Saraiva Rommel D. Gimaeva Susanna Map Generation and Balance in the Terra Mystica Board Game Using Particle Swarm and Local Search 2020 en DOI.org (Crossref) http://link.springer.com/10.1007/978-3-030-53956-6_15 2023-04-17 06:15:24 Series Title: Lecture Notes in Computer Science DOI: 10.1007/978-3-030-53956-6_15 163-175 conferencePaper ISBN 978-1-4503-8807-8 International Conference on the Foundations of Digital Games DOI 10.1145/3402942.3409778 Bugibba Malta ACM Saraiva Rommel Dias Grichshenko Alexandr Araújo Luiz Jonatã Pires de Amaro Junior Bonfim de Carvalho Guilherme Nepomuceno Using Ant Colony Optimisation for map generation and improving game balance in the Terra Mystica and Settlers of Catan board games 2020-09-15 en DOI.org (Crossref) https://dl.acm.org/doi/10.1145/3402942.3409778 2023-04-17 06:15:08 1-7 FDG '20: International Conference on the Foundations of Digital Games report Greg Heon Lilli Oetting An Application of Machine Learning to the Board Game Pentago We present an application of machine learning to the two-player strategy game Pentago. We have explored two different models through which to train our program - a feature-based model and a reinforcement learning model. This paper discusses the relative merits of each, as well as the obstacles we encountered using each model. http://cs229.stanford.edu/proj2012/HeonOetting-AnAppliactionOfMachineLearningToTheBoardGamePentago.pdf attachment HeonOetting-AnAppliactionOfMachineLearningToTheBoardGamePentago.pdf http://cs229.stanford.edu/proj2012/HeonOetting-AnAppliactionOfMachineLearningToTheBoardGamePentago.pdf 2023-04-17 06:17:45 3 report Jimbo, Shuji Learning finite functions by neural networks : Evaluation of Pentago positions by convolutional neural networks A convolution neural network (CNN) is a useful tool that approximates a finite function. It is used as a solver for various problems in the real world. In this paper, results of experiments on training variations of a small CNN used for image recognition for evaluating Pentago positions are mainly reported. The author hopes that the results are used in discussion of applicability of deep neural networks to researches in theoretical computer science. https://repository.kulib.kyoto-u.ac.jp/dspace/handle/2433/251730 attachment 2096-03.pdf https://repository.kulib.kyoto-u.ac.jp/dspace/bitstream/2433/251730/1/2096-03.pdf 2023-04-17 06:20:42 3 journalArticle Gaina Raluca D. Goodman James Perez-Liebana Diego TAG: Terraforming Mars Games and Artificial Intelligence (AI) have had a tight relationship for many years. A multitude of games have been used as environments in which AI players can learn to act and interact with others or the game mechanics directly; used as optimisation problems; used as generators of large amounts of data which can be analysed to learn about the game, or about the players; or used as containers of content which can be automatically generated by AI methods. Yet many of these environments have been very simple and limited in scope. We propose here a much more complex environment based on the boardgame Terraforming Mars, implemented as part of the Tabletop Games Framework: a very large and dynamic action space, hidden information, large amounts of content, resource management and high variability make this problem domain stand out in the current landscape and a very interesting problem for AI methods of multiple domains. We include results of baseline AI game-players in this game and in-depth analysis of the game itself, together with an exploration of problem complexity, challenges and opportunities. 2021-10-04 TAG DOI.org (Crossref) https://ojs.aaai.org/index.php/AIIDE/article/view/18902 2023-04-17 06:27:48 148-155 17 Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment DOI 10.1609/aiide.v17i1.18902 1 AIIDE ISSN 2334-0924, 2326-909X attachment Full Text https://ojs.aaai.org/index.php/AIIDE/article/download/18902/18667 2023-04-17 06:27:56 1 application/pdf bookSection 74 ISBN 978-3-319-59393-7 978-3-319-59394-4 Agent and Multi-Agent Systems: Technology and Applications Cham Springer International Publishing Jezic Gordan Kusek Mario Chen-Burger Yun-Heh Jessica Howlett Robert J. Jain Lakhmi C. Dreżewski Rafał Klęczar Maciej Artificial Intelligence Techniques for the Puerto Rico Strategy Game 2018 en DOI.org (Crossref) http://link.springer.com/10.1007/978-3-319-59394-4_8 2023-04-17 06:31:21 Series Title: Smart Innovation, Systems and Technologies DOI: 10.1007/978-3-319-59394-4_8 77-87 conferencePaper Mavromoustakos Blom Multiplayer Tension In the Wild: A Hearthstone Case Games are designed to elicit strong emotions during game play, especially when players are competing against each other. Artificial Intelligence applied to predict a player's emotions has mainly been tested on single-player experiences in low-stakes settings and short-term interactions. How do players experience and manifest affect in high-stakes competitions, and which modalities can capture this? This paper reports a first experiment in this line of research, using a competition of the video game Hearthstone where both competing players' game play and facial expressions were recorded over the course of the entire match which could span up to 41 minutes. Using two experts' annotations of tension using a continuous video affect annotation tool, we attempt to predict tension from the webcam footage of the players alone. Treating both the input and the tension output in a relative fashion, our best models reach 66.3% average accuracy (up to 79.2% at the best fold) in the challenging leave-one-participant out cross-validation task. This initial experiment shows a way forward for affect annotation in games {"}in the wild{"} in high-stakes, real-world competitive settings. 2023 https://research.tilburguniversity.edu/en/publications/multiplayer-tension-in-the-wild-a-hearthstone-case Foundations of Digital Games attachment FDG_Modelling_Player_Tension_Through_Facial_Expressions_in_Competitive_Hearthstone_2_.pdf https://pure.uvt.nl/ws/portalfiles/portal/69103237/FDG_Modelling_Player_Tension_Through_Facial_Expressions_in_Competitive_Hearthstone_2_.pdf 2023-04-18 04:45:05 3 preprint arXiv Świechowski Maciej Tajmajer Tomasz Janusz Andrzej Computer Science - Artificial Intelligence Computer Science - Machine Learning Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms We investigate the impact of supervised prediction models on the strength and efficiency of artificial agents that use the Monte-Carlo Tree Search (MCTS) algorithm to play a popular video game Hearthstone: Heroes of Warcraft. We overview our custom implementation of the MCTS that is well-suited for games with partially hidden information and random effects. We also describe experiments which we designed to quantify the performance of our Hearthstone agent's decision making. We show that even simple neural networks can be trained and successfully used for the evaluation of game states. Moreover, we demonstrate that by providing a guidance to the game state search heuristic, it is possible to substantially improve the win rate, and at the same time reduce the required computations. 2018-08-14 arXiv.org http://arxiv.org/abs/1808.04794 2023-04-18 04:46:43 arXiv:1808.04794 [cs] arXiv:1808.04794 attachment arXiv Fulltext PDF https://arxiv.org/pdf/1808.04794.pdf 2023-04-18 04:46:45 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/1808.04794 2023-04-18 04:46:52 1 text/html thesis New Jersey Institute of Technology Watson, Connor W. Analysis of gameplay strategies in hearthstone: a data science approach In recent years, games have been a popular test bed for AI research, and the presence of Collectible Card Games (CCGs) in that space is still increasing. One such CCG for both competitive/casual play and AI research is Hearthstone, a two-player adversarial game where players seeks to implement one of several gameplay strategies to defeat their opponent and decrease all of their Health points to zero. Although some open source simulators exist, some of their methodologies for simulated agents create opponents with a relatively low skill level. Using evolutionary algorithms, this thesis seeks to evolve agents with a higher skill level than those implemented in one such simulator, SabberStone. New benchmarks are propsed using supervised learning techniques to predict gameplay strategies from game data, and using unsupervised learning techniques to discover and visualize patterns that may be used in player modeling to differentiate gameplay strategies. http://archives.njit.edu/vol01/etd/2020s/2020/njit-etd2020-006/njit-etd2020-006.pdf journalArticle Eiji Sakurai Koji Hasabe Decision-Making in Hearthstone Based on Evolutionary Algorithm Hearthstone is a two-player turn-based collectible card game with hidden information and randomness. Gen- erally, the search space for actions of this game grows exponentially because the players must perform a series of actions by selecting each action from many options in each turn. When playing such a game, it is often difficult to use a game tree search technique to find the optimal sequence of actions up until the end of a turn. To solve this problem, we propose a method to determine a series of actions in Hearthstone based on an evolu- tionary algorithm called the rolling horizon evolutionary algorithm (RHEA). To apply RHEA to this game, we modify the genetic operators and add techniques for selecting actions based on previous search results and for filtering (pruning) some of the action options. To evaluate the effectiveness of these improvements, we imple- mented an agent based on the proposed method and played it against an agent based on the original RHEA for several decks. The result showed a maximum winning rate of over 97.5%. Further, our agent played against the top-performing agents in previous competitions and outperformed most of them. 2023 https://www.cs.tsukuba.ac.jp/~hasebe/downloads/icaart2023_sakurai.pdf conferencePaper ISBN 978-1-5090-1883-3 2016 IEEE Conference on Computational Intelligence and Games (CIG) DOI 10.1109/CIG.2016.7860416 Santorini, Greece IEEE Bursztein Elie I am a legend: Hacking hearthstone using statistical learning methods 9/2016 I am a legend DOI.org (Crossref) http://ieeexplore.ieee.org/document/7860416/ 2023-04-18 04:50:48 1-8 2016 IEEE Conference on Computational Intelligence and Games (CIG) journalArticle Hoover Amy K. Togelius Julian Lee Scott de Mesentier Silva Fernando The Many AI Challenges of Hearthstone 03/2020 en DOI.org (Crossref) http://link.springer.com/10.1007/s13218-019-00615-z 2023-04-18 04:51:07 33-43 34 KI - Künstliche Intelligenz DOI 10.1007/s13218-019-00615-z 1 Künstl Intell ISSN 0933-1875, 1610-1987 attachment Submitted Version https://arxiv.org/pdf/1907.06562 2023-04-18 04:51:11 1 application/pdf conferencePaper ISBN 978-1-5090-1883-3 2016 IEEE Conference on Computational Intelligence and Games (CIG) DOI 10.1109/CIG.2016.7860426 Santorini, Greece IEEE Garcia-Sanchez Pablo Tonda Alberto Squillero Giovanni Mora Antonio Merelo Juan J. Evolutionary deckbuilding in hearthstone 9/2016 DOI.org (Crossref) http://ieeexplore.ieee.org/document/7860426/ 2023-04-18 04:52:11 1-8 2016 IEEE Conference on Computational Intelligence and Games (CIG) attachment Evolutionary-Deckbuilding-in-HearthStone.pdf https://www.researchgate.net/profile/Alberto-Tonda/publication/304246423_Evolutionary_Deckbuilding_in_HearthStone/links/5a5bc15faca2727d608a25b6/Evolutionary-Deckbuilding-in-HearthStone.pdf 2023-04-18 04:54:19 3 preprint arXiv Silva Fernando de Mesentier Canaan Rodrigo Lee Scott Fontaine Matthew C. Togelius Julian Hoover Amy K. Computer Science - Artificial Intelligence Computer Science - Neural and Evolutionary Computing Evolving the Hearthstone Meta Balancing an ever growing strategic game of high complexity, such as Hearthstone is a complex task. The target of making strategies diverse and customizable results in a delicate intricate system. Tuning over 2000 cards to generate the desired outcome without disrupting the existing environment becomes a laborious challenge. In this paper, we discuss the impacts that changes to existing cards can have on strategy in Hearthstone. By analyzing the win rate on match-ups across different decks, being played by different strategies, we propose to compare their performance before and after changes are made to improve or worsen different cards. Then, using an evolutionary algorithm, we search for a combination of changes to the card attributes that cause the decks to approach equal, 50% win rates. We then expand our evolutionary algorithm to a multi-objective solution to search for this result, while making the minimum amount of changes, and as a consequence disruption, to the existing cards. Lastly, we propose and evaluate metrics to serve as heuristics with which to decide which cards to target with balance changes. 2019-07-02 arXiv.org http://arxiv.org/abs/1907.01623 2023-04-18 04:54:36 arXiv:1907.01623 [cs] arXiv:1907.01623 attachment arXiv Fulltext PDF https://arxiv.org/pdf/1907.01623.pdf 2023-04-18 04:54:40 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/1907.01623 2023-04-18 04:54:46 1 text/html journalArticle García-Sánchez Pablo Tonda Alberto Fernández-Leiva Antonio J. Cotta Carlos Optimizing Hearthstone agents using an evolutionary algorithm 01/2020 en DOI.org (Crossref) https://linkinghub.elsevier.com/retrieve/pii/S0950705119304356 2023-04-18 04:55:18 105032 188 Knowledge-Based Systems DOI 10.1016/j.knosys.2019.105032 Knowledge-Based Systems ISSN 09507051 attachment garcia19optimizing.pdf http://www.lcc.uma.es/~ccottap/papers/garcia19optimizing.pdf 2023-04-18 04:55:24 3 conferencePaper ISBN 978-1-4503-6571-0 Proceedings of the 13th International Conference on the Foundations of Digital Games DOI 10.1145/3235765.3235791 Malmö Sweden ACM Bhatt Aditya Lee Scott de Mesentier Silva Fernando Watson Connor W. Togelius Julian Hoover Amy K. Exploring the hearthstone deck space 2018-08-07 en DOI.org (Crossref) https://dl.acm.org/doi/10.1145/3235765.3235791 2023-04-18 04:55:33 1-10 FDG '18: Foundations of Digital Games 2018 attachment Exploring-the-hearthstone-deck-space.pdf https://www.researchgate.net/profile/Fernando-De-Mesentier-Silva/publication/327637789_Exploring_the_hearthstone_deck_space/links/5c50b295a6fdccd6b5d1e5a2/Exploring-the-hearthstone-deck-space.pdf 2023-04-18 04:55:46 3 thesis Politecnico di Torino Stefano Griva Computational Intelligence Techniques for Games with Incomplete Information Artificial intelligence is an ever growing field in computer science, with new techniques and algorithms getting developed every day. Our aim is to show how AIs can improve their performances by using hidden information, that would normally require complex human deduction to normally exploit. Modern game AIs often rely on clear and curated data, deterministic information and overall accurate numbers to make their calculations, however there are a lot of games that involve pieces of information that are incomplete or hidden. Incomplete information can be extremely helpful to an AI, but it requires additional care when taken into consideration because it's usually based on statistical analysis and heuristics. Our focus is set on a few innovative computational intelligence techniques that aim at improving the efficiency of hidden information-based AIs, by allowing them to explore non-deterministic scenarios: 1) The Double Inverted Index is an algorithm that can be used in hidden information games, such as card games, to narrow the great possibilities and scenarios to calculate down to a reasonable number. This approach is based on how humans would think in similar situation. 2) The Blunder Threshold is a technique that helps the AI navigating probabilistic scenarios balancing the pros and cons of deeper analysis and uncertain information. We'll explore different parameters and options of the previously mentioned techniques as well as showing their efficacy in practice with a focus on the chosen test game Hearthstone. en https://webthesis.biblio.polito.it/26844/ 60 Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering) attachment PDF https://webthesis.biblio.polito.it/secure/26844/1/tesi.pdf 2023-05-06 06:11:46 3 preprint arXiv Zhang Zhujun Computer Science - Computational Complexity Perfect Information Hearthstone is PSPACE-hard We consider the computational complexity of Hearthstone which is a popular online CCG (collectible card game). We reduce a PSPACE-complete problem, the partition game, to perfect information Hearthstone in which there is no hidden information or random elements. In the reduction, each turn in Hearthstone is used to simulate one choice in the partition game. It is proved that determining whether the player has a forced win in perfect information Hearthstone is PSPACE-hard. 2023-05-22 arXiv.org http://arxiv.org/abs/2305.12731 2023-05-31 06:12:11 arXiv:2305.12731 [cs] arXiv:2305.12731 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2305.12731.pdf 2023-05-31 06:12:22 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2305.12731 2023-05-31 06:12:28 1 text/html preprint arXiv Kowalski Jakub Miernik Radosław Computer Science - Artificial Intelligence Summarizing Strategy Card Game AI Competition This paper concludes five years of AI competitions based on Legends of Code and Magic (LOCM), a small Collectible Card Game (CCG), designed with the goal of supporting research and algorithm development. The game was used in a number of events, including Community Contests on the CodinGame platform, and Strategy Card Game AI Competition at the IEEE Congress on Evolutionary Computation and IEEE Conference on Games. LOCM has been used in a number of publications related to areas such as game tree search algorithms, neural networks, evaluation functions, and CCG deckbuilding. We present the rules of the game, the history of organized competitions, and a listing of the participant and their approaches, as well as some general advice on organizing AI competitions for the research community. Although the COG 2022 edition was announced to be the last one, the game remains available and can be played using an online leaderboard arena. 2023-05-19 arXiv.org http://arxiv.org/abs/2305.11814 2023-05-31 06:13:47 arXiv:2305.11814 [cs] arXiv:2305.11814 attachment arXiv Fulltext PDF https://arxiv.org/pdf/2305.11814.pdf 2023-05-31 06:13:56 1 application/pdf attachment arXiv.org Snapshot https://arxiv.org/abs/2305.11814 2023-05-31 06:14:02 1 text/html conferencePaper ISBN 978-1-4503-9421-5 Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems DOI 10.1145/3544548.3581550 Hamburg Germany ACM Sidji Matthew Smith Wally Rogerson Melissa J. The Hidden Rules of Hanabi: How Humans Outperform AI Agents 2023-04-19 en The Hidden Rules of Hanabi DOI.org (Crossref) https://dl.acm.org/doi/10.1145/3544548.3581550 2023-05-31 06:19:29 1-16 CHI '23: CHI Conference on Human Factors in Computing Systems journalArticle Vieira Ronaldo E Silva Rocha Tavares Anderson Chaimowicz Luiz Towards sample efficient deep reinforcement learning in collectible card games 7/2023 en DOI.org (Crossref) https://linkinghub.elsevier.com/retrieve/pii/S1875952123000496 2023-07-27 12:13:09 100594 Entertainment Computing DOI 10.1016/j.entcom.2023.100594 Entertainment Computing ISSN 18759521 2048 Accessibility Azul Blokus Carcassonne Diplomacy Dixit Dominion Frameworks Game Design General Gameplay Hanabi Hearthstone Hive Jenga Kingdomino Lost Cities Mafia Magic: The Gathering Mobile Games Modern Art: The card game Monopoly Monopoly Deal Netrunner Nmbr9 Pandemic Patchwork Pentago Puerto Rico Quixo Race for the Galaxy Resistance: Avalon RISK Santorini Scotland Yard Secret Hitler Set Settlers of Catan Shobu Terra Mystica Terraforming Mars Tetris Link Ticket to Ride Ultimate Tic-Tac-Toe UNO Yahtzee