Details
Title
Optimisation of MCTS player for The Lord of the Rings: The Card GameJournal title
Bulletin of the Polish Academy of Sciences Technical SciencesYearbook
2021Volume
69Issue
3Affiliation
Godlewski, Konrad : Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warsaw, Poland ; Sawicki, Bartosz : Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warsaw, PolandAuthors
Keywords
Computational Intelligence ; Monte-Carlo Tree Search ; LoTRDivisions of PAS
Nauki TechniczneCoverage
e136752Bibliography
- C. Browne, “A survey of monte carlo tree search methods”, IEEE Trans. Comput. Intell. AI Games 4., 1–43 (2012).
- R. Bjarnason, A. Fern, and P. Tadepalli, “Lower bounding Klondike solitaire with Monte-Carlo planning”, Nineteenth International Conference on Automated Planning and Scheduling, 2009.
- M. Świechowski, T. Tajmajer, and A. Janusz, “Improving hearthstone ai by combining mcts and supervised learning algo rithms”, 2018 IEEE Conference on Computational Intelligence and Games (CIG), 2018.
- J. Mańdziuk, “MCTS/UCT in Solving Real-Life Problems”, Advances in Data Analysis with Computational Intelligence Methods, 277‒292, Springer, Cham, 2018.
- S. Kajita, T. Kinjo, and T. Nishi, “Autonomous molecular design by Monte-Carlo tree search and rapid evaluations using molecular dynamics simulations”, Commun. Phys. 3(1), 1‒11 (2020).
- S. Haeri and L. Trajković, “Virtual network embedding via Monte Carlo tree search”, IEEE Trans. Cybern. 48(2), 510‒521 (2017).
- G. Best, O.M. Cliff, T. Patten, R.R. Mettu, and R. Fitch, “Decentralised Monte Carlo tree search for active perception”, Algorithmic Foundations of Robotics XII, 864‒879, Springer, Cham, 2020.
- D.A. Dhar, P. Morawiecki, and S. Wójtowicz. “Finding differential paths in arx ciphers through nested monte-carlo search”, AEU Int. J. Electron. Commun 64(2), 147‒150 (2018).
- K. Guzek and P. Napieralski, “Measurement of noise in the Monte Carlo point sampling method”, Bull. Pol. Acad. Sci. Tech. Sci. 59(1), 15‒19 (2011).
- D. Tefelski, T. Piotrowski, A. Polański, J. Skubalski and V. Blideanu, “Monte-Carlo aided design of neutron shielding concretes”, Bull. Pol. Acad. Sci. Tech. Sci. 61(1), 161‒171 (2013).
- C.D. Ward and P.I. Cowling, “Monte Carlo search applied to card selection in Magic: The Gathering”, IEEE Symposium on Computational Intelligence and Games, 2009.
- P.I. Cowling, C.D. Ward, and E.J. Powley, “Ensemble determinization in monte carlo tree search for the imperfect information card game magic: The gathering”, IEEE Trans. Comput. Intell. AI Games 4(4), 241‒257 (2012).
- S. Turkay, S. Adinolf, and D. Tirthali, “Collectible Card Games as Learning Tools”, Procedia – Soc. Behav. Sci. 46, 3701‒3705 (2012), doi: 10.1016/j.sbspro.2012.06.130.
- K. Bochennek, B. Wittekindt, S.-Y. Zimmermann, and T. Klingebiel, “More than mere games: a review of card and board games for medical education”, Med. Teach. 29(9), 941‒948 (2007), doi: 10.1080/01421590701749813.
- J.S.B. Choe and J. Kim, “Enhancing Monte Carlo Tree Search for Playing Hearthstone”, 2019 IEEE Conference on Games (CoG), London, United Kingdom, 2019, pp. 1‒7.
- K. Godlewski and B. Sawicki, “MCTS Based Agents for Multistage Single-Player Card Game”, 21st International Conference on Computational Problems of Electrical Engineering (CPEE), 2020
- “Magic: The Gathering”, [online] https://magic.wizards.com/en
- E.J. Powley, P.I. Cowling, and D. Whitehouse. “Information capture and reuse strategies in Monte Carlo Tree Search, with applications to games of hidden information”, Artif. Intell. 217, 92‒116 (2014).
- Fantasy Flight Publishing, “Hall of Beorn”, technical documentation, 2020 [Online] Available: http://hallofbeorn.com/LotR/Scenarios/ Passage-Through-Mirkwood
- S. Zhang and M. Buro, “Improving hearthstone AI by learning high-level rollout policies and bucketing chance node events”, 2017 IEEE Conference on Computational Intelligence and Games (CIG), New York, USA, 2017, pp. 309‒316.
- G.M.J-B. Chaslot, M.H.M. Winands, and H.J. van Den Herik, “Parallel monte-carlo tree search”, International Conference on Computers and Games, Springer, Berlin, Heidelberg, 2008.
- A. Fern and P. Lewis, “Ensemble monte-carlo planning: An empirical study”, Twenty-First International Conference on Automated Planning and Scheduling, ICAPS 2011, Germany, 2011.
- A. Santos, P. A. Santos, and F.S. Melo, “Monte Carlo tree search experiments in hearthstone,” 2017 IEEE Conference on Computational Intelligence and Games (CIG), New York, USA, 2017, pp. 272‒279.