Fetching the paper…
Reading the bibliography…
Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-playing bots or solving sequential decision problems.
1907
Earlier work this paper cites.
Thompson WR (1933) On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25(3/4):285–294
1933
Earlier work this paper cites.
Tversky A, Kahneman D (1974) Judgment under uncertainty: Heuristics and biases. science 185(4157):1124–1131
1974
Earlier work this paper cites.
Lowerre BT (1976) The harpy speech recognition system. Tech. rep., CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE
1976
Earlier work this paper cites.
Schaeffer J (1989) The history heuristic and alpha-beta search enhancements in practice. IEEE transactions on pattern analysis and machine intelligence 11(11):1203–1212
1989
Earlier work this paper cites.
Brügmann B (1993) Monte Carlo Go. Tech. rep., Citeseer
1993
Earlier work this paper cites.
Tesauro G (1994) TD-Gammon, a self-teaching Backgammon program, achieves master-level play. Neural computation 6(2):215–219
1994
Earlier work this paper cites.
Wolpert DH, Macready WG (1997) No Free Lunch Theorems for Optimization. IEEE Transactions on Evolutionary Computation 1(1):67–82
1997
Earlier work this paper cites.
Frank I, Basin D (1998) Search in games with incomplete information: A case study using Bridge card play. Artificial Intelligence 100(1-2):87–123
1998
Earlier work this paper cites.
Junghanns A (1998) Are there practical alternatives to alpha-beta? ICGA Journal 21(1):14–32
1998
Earlier work this paper cites.
Graepel T, Goutrie M, Krüger M, Herbrich R (2001) Learning on graphs in the game of Go. In: International Conference on Artificial Neural Networks, Springer, pp 347–352
2001
Earlier work this paper cites.
Auer P, Cesa-Bianchi N, Fischer P (2002) Finite-Time Analysis of the Multiarmed Bandit Problem. Machine Learning 47(2-3):235–256
2002
Earlier work this paper cites.
Campbell M, Hoane Jr AJ, Hsu Fh (2002) Deep blue. Artificial intelligence 134(1-2):57–83
2002
Earlier work this paper cites.
Kishimoto A, Schaeffer J (2002) Transposition Table Driven Work Scheduling in Distributed Game-Tree Search. In: Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence, Springer-Verlag, London, UK, UK, AI ‘02, pp 56–68
2002
Earlier work this paper cites.
Syed O, Syed A (2003) Arimaa - A New Game Designed to be Difficult for Computers, vol 26. Institute for Knowledge and Agent Technology
2003
Earlier work this paper cites.
Ghallab M, Nau D, Traverso P (2004) Automated Planning: theory and practice. Elsevier
2004
Earlier work this paper cites.
Hunicke R (2005) The case for dynamic difficulty adjustment in games. In: Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology, pp 429–433
2005
Earlier work this paper cites.
Coulom R (2006) Efficient Selectivity and Backup Operators in Monte Carlo Tree Search. In: International conference on computers and games, Springer, pp 72–83
2006
Earlier work this paper cites.
Gelly S, Wang Y (2006) Exploration Exploitation in Go: UCT for Monte-Carlo Go. In: NIPS: Neural Information Processing Systems Conference On-line trading of Exploration and Exploitation Workshop, Canada, URL https://hal.archives-ouvertes.fr/hal-00115330
2006
Earlier work this paper cites.
Kocsis L, Szepesvári C (2006) Bandit based Monte-Carlo planning. In: Proceedings of the 17th European conference on Machine Learning, Springer-Verlag, Berlin, Heidelberg, ECML‘06, pp 282–293
2006
Earlier work this paper cites.
Cazenave T, Jouandeau N (2007) On the Parallelization of UCT. Proceedings of CGW07 pp 93–101
2007
Earlier work this paper cites.
Coulom R (2007) Computing “elo ratings” of move patterns in the game of Go. ICGA journal 30(4):198–208
2007
Earlier work this paper cites.
Drake P, Uurtamo S (2007) Move ordering vs heavy playouts: Where should heuristics be applied in Monte Carlo Go. In: Proceedings of the 3rd North American Game-On Conference, Citeseer, pp 171–175
2007
Earlier work this paper cites.
Schaeffer J, Burch N, Björnsson Y, Kishimoto A, Müller M, Lake R, Lu P, Sutphen S (2007) Checkers is solved. Science 317(5844):1518–1522, URL https://science.sciencemag.org/content/317/5844/1518 , https://science.sciencemag.org/content/317/5844/1518.full.pdf
2007
Earlier work this paper cites.
Cazenave T, Jouandeau N (2008) A parallel Monte Carlo Tree Search algorithm. In: International Conference on Computers and Games, Springer, pp 72–80
2008
Earlier work this paper cites.
Finnsson H, Björnsson Y (2008) Simulation-based approach to General Game Playing. In: Aaai, vol 8, pp 259–264
2008
Earlier work this paper cites.
Paruchuri P, Pearce JP, Marecki J, Tambe M, Ordonez F, Kraus S (2008) Playing games for security: an efficient exact algorithm for solving Bayesian Stackelberg games. In: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems-Vol. 2, pp 895–902
2008
Earlier work this paper cites.
Van den Broeck G, Driessens K, Ramon J (2009) Monte Carlo Tree Search in Poker Using Expected Reward Distributions. In: Asian Conference on Machine Learning, Springer, pp 367–381
2009
Earlier work this paper cites.
Chaslot GMB, Hoock JB, Perez J, Rimmel A, Teytaud O, Winands MH (2009) Meta Monte Carlo Tree Search for Automatic Opening Book Generation. In: Proc. 21st Int. Joint Conf. Artif. Intell., Pasadena, California, pp 7–12
2009
Earlier work this paper cites.
Szita I, Chaslot G, Spronck P (2009) Monte Carlo Tree Search in Settlers of Catan. Advances in Computer Games pp 21–32
2009
Earlier work this paper cites.
Arneson B, Hayward RB, Henderson P (2010) Monte Carlo Tree Search in Hex. IEEE Transactions on Computational Intelligence and AI in Games 2(4):251–258
2010
Earlier work this paper cites.
Finnsson H, Björnsson Y (2010) Learning Simulation Control in General Game-Playing Agents. In: AAAI, vol 10, pp 954–959
2010
Earlier work this paper cites.
Gaudel R, Hoock JB, Pérez J, Sokolovska N, Teytaud O (2010) A Principled Method for Exploiting Opening Books. In: International conference on computers and games, Springer, pp 136–144
2010
Earlier work this paper cites.
Jain M, Tsai J, Pita J, Kiekintveld C, Rathi S, Tambe M, Ordóñez F (2010) Software assistants for randomized patrol planning for the lax airport police and the federal air marshal service. Interfaces 40(4):267–290
2010
Earlier work this paper cites.
Teytaud F, Teytaud O (2010) Creating an Upper-Confidence-Tree Program for Havannah. In: Advances in Computer Games, Springer, pp 65–74
2010
Earlier work this paper cites.
Thielscher M (2010) A general game description language for incomplete information games. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 24
2010
Earlier work this paper cites.
Winands MH, Bjornsson Y, Saito JT (2010) Monte Carlo Tree Search in Lines of Action. IEEE Transactions on Computational Intelligence and AI in Games 2(4):239–250
2010
Earlier work this paper cites.
Finnsson H, Björnsson Y (2011) Cadiaplayer: Search-Control Techniques. KI-Künstliche Intelligenz 25(1):9–16
2011
Earlier work this paper cites.
Gelly S, Silver D (2011) Monte Carlo Tree Search and Rapid Action Value Estimation in Computer Go. Artificial Intelligence 175(11):1856–1875
2011
Earlier work this paper cites.
Robles D, Rohlfshagen P, Lucas SM (2011) Learning Non-Random Moves for Playing Othello: Improving Monte Carlo Tree Search. In: 2011 IEEE Conference on Computational Intelligence and Games (CIG‘11), IEEE, pp 305–312
2011
Earlier work this paper cites.
Waledzik K, Mańdziuk J (2011) Multigame playing by means of UCT enhanced with automatically generated evaluation functions. In: Artificial General Intelligence - 4th International Conference, AGI 2011, Mountain View, CA, USA, Springer, Lecture Notes in Computer Science, vol 6830, pp 327–332
2011
Earlier work this paper cites.
Baier H, Winands MH (2012) Beam Monte Carlo Tree Search. In: 2012 IEEE Conference on Computational Intelligence and Games (CIG), IEEE, pp 227–233
2012
Earlier work this paper cites.
Browne C (2012) A problem case for UCT. IEEE Transactions on Computational Intelligence and AI in Games 5(1):69–74
2012
Earlier work this paper cites.
Browne CB, Powley E, Whitehouse D, Lucas SM, Cowling PI, Rohlfshagen P, Tavener S, Perez D, Samothrakis S, Colton S (2012) A Survey of Monte Carlo Tree Search Methods. IEEE Transactions on Computational Intelligence and AI in Games 4(1):1–43
2012
Earlier work this paper cites.
Chen KH (2012) Dynamic randomization and domain knowledge in Monte Carlo Tree Search for Go knowledge-based systems. Knowledge-Based Systems 34:21–25
2012
Earlier work this paper cites.
Gelly S, Kocsis L, Schoenauer M, Sebag M, Silver D, Szepesvári C, Teytaud O (2012) The grand challenge of computer Go: Monte Carlo Tree Search and extensions. Communications ACM 55(3):106–113
2012
Earlier work this paper cites.
Keller T, Eyerich P (2012) PROST: Probabilistic Planning Based on UCT. In: ICAPS, pp 119–127
2012
Earlier work this paper cites.
Nguyen KQ, Thawonmas R (2012) Monte Carlo Tree Search for collaboration control of ghosts in MS Pac-Man. IEEE Transactions on Computational Intelligence and AI in Games 5(1):57–68
2012
Earlier work this paper cites.
Pepels T, Winands MH (2012) Enhancements for Monte Carlo Tree Search in MS Pac-Man. In: 2012 IEEE Conference on Computational Intelligence and Games (CIG), IEEE, pp 265–272
2012
Earlier work this paper cites.
Perick P, St-Pierre DL, Maes F, Ernst D (2012) Comparison of different selection strategies in Monte Carlo Tree Search for the Game of Tron. In: 2012 IEEE Conference on Computational Intelligence and Games (CIG), IEEE, pp 242–249
2012
Earlier work this paper cites.
Pettit J, Helmbold D (2012) Evolutionary Learning of Policies for MCTS Simulations. In: Proceedings of the International Conference on the Foundations of Digital Games, pp 212–219
2012
Earlier work this paper cites.
Silver D, Sutton RS, Müller M (2012) Temporal-difference search in computer Go. Machine learning 87(2):183–219, DOI https://doi.org/10.1007/s10994-012-5280-0
2012
Earlier work this paper cites.
Tak MJ, Winands MH, Bjornsson Y (2012) N-grams and the last-good-reply policy applied in General Game Playing. IEEE Transactions on Computational Intelligence and AI in Games 4(2):73–83
2012
Earlier work this paper cites.
Tak MJW, Winands MHM, Bjornsson Y (2012) N-Grams and the Last-Good-Reply Policy Applied in General Game Playing. IEEE Transactions on Computational Intelligence and AI in Games 4(2):73–83
2012
Earlier work this paper cites.
Alhejali AM, Lucas SM (2013) Using genetic programming to evolve heuristics for a Monte Carlo Tree Search Ms Pac-Man agent. In: 2013 IEEE Conference on Computational Inteligence in Games (CIG), IEEE, pp 1–8
2013
Earlier work this paper cites.
Amer MR, Todorovic S, Fern A, Zhu SC (2013) Monte Carlo Tree Search for scheduling activity recognition. In: Proceedings of the IEEE international conference on computer vision, pp 1353–1360
2013
Earlier work this paper cites.
An B, Ordóñez F, Tambe M, Shieh E, Yang R, Baldwin C, DiRenzo J, Moretti K, Maule B, Meyer G (2013) A deployed quantal response-based patrol planning system for the U.S. coast guard. Interfaces 43(5):400–420
2013
Earlier work this paper cites.
Bai A, Wu F, Chen X (2013) Bayesian mixture modelling and inference based thompson sampling in monte-carlo tree search. Proceedings of the Advances in Neural Information Processing Systems (NIPS) pp 1646–1654
2013
Earlier work this paper cites.
Baier H, Mark HW (2013) Monte Carlo Tree Search and minimax hybrids. In: 2013 IEEE Conference on Computational Intelligence in Games (CIG), IEEE, pp 1–8
2013
Earlier work this paper cites.
Benbassat A, Sipper M (2013) EvoMCTS: Enhancing MCTS-based players through genetic programming. In: 2013 IEEE Conference on Computational Inteligence in Games (CIG), IEEE, pp 1–8
2013
Earlier work this paper cites.
Churchill D, Buro M (2013) Portfolio greedy search and simulation for large-scale combat in Starcraft. In: 2013 IEEE Conference on Computational Inteligence in Games (CIG), IEEE, pp 1–8
2013
Earlier work this paper cites.
Furtak T, Buro M (2013) Recursive Monte Carlo search for imperfect information games. In: 2013 IEEE Conference on Computational Inteligence in Games (CIG), pp 1–8
2013
Earlier work this paper cites.
Gudmundsson SF, Björnsson Y (2013) Sufficiency-based selection strategy for MCTS. In: Twenty-Third International Joint Conference on Artificial Intelligence, Citeseer
2013
Earlier work this paper cites.
Huang SC, Arneson B, Hayward RB, Müller M, Pawlewicz J (2013) MoHex 2.0: a Pattern-Based MCTS Hex Player. In: International Conference on Computers and Games, Springer, pp 60–71
2013
Earlier work this paper cites.
Ikeda K, Viennot S (2013b) Production of various strategies and position control for Monte-Carlo Go—entertaining human players. In: 2013 IEEE Conference on Computational Inteligence in Games (CIG), IEEE, pp 1–8
2013
Earlier work this paper cites.
Juan AA, Faulin J, Jorba J, Caceres J, Marquès JM (2013) Using parallel & distributed computing for real-time solving of vehicle routing problems with stochastic demands. Annals of Operations Research 207(1):43–65
2013
Earlier work this paper cites.
Kao KY, Wu IC, Yen SJ, Shan YC (2013) Incentive learning in Monte Carlo Tree Search. IEEE Transactions on Computational Intelligence and AI in Games 5(4):346–352
2013
Earlier work this paper cites.
Kuipers J, Plaat A, Vermaseren J, van den Herik H (2013) Improving multivariate Horner schemes with Monte Carlo Tree Search. Computer Physics Communications 184(11):2391 – 2395, URL http://www.sciencedirect.com/science/article/pii/S0010465513001689
2013
Earlier work this paper cites.
Ontañón S, Synnaeve G, Uriarte A, Richoux F, Churchill D, Preuss M (2013) A survey of real-time strategy game AI research and competition in Starcraft. IEEE Transactions on Computational Intelligence and AI in Games 5(4):293–311
2013
Earlier work this paper cites.
Perez D, Samothrakis S, Lucas S, Rohlfshagen P (2013) Rolling Horizon Evolution Versus Tree Search for Navigation in Single-Player Real-Time Games. In: Proceedings of the 15th annual conference on Genetic and evolutionary computation, pp 351–358
2013
Earlier work this paper cites.
Powley EJ, Whitehouse D, Cowling PI (2013a) Bandits all the way down: UCB1 as a simulation policy in Monte Carlo Tree Search. In: 2013 IEEE Conference on Computational Inteligence in Games (CIG), IEEE, pp 1–8
2013
Earlier work this paper cites.
Powley EJ, Whitehouse D, Cowling PI (2013b) Monte Carlo Tree Search with macro-actions and heuristic route planning for the multiobjective physical travelling salesman problem. In: 2013 IEEE Conference on Computational Inteligence in Games (CIG), IEEE, pp 1–8
2013
Earlier work this paper cites.
Rabin S (2013) Game AI Pro: Collected Wisdom of Game AI Professionals. CRC Press
2013
Earlier work this paper cites.
Wijaya TK, Papaioannou TG, Liu X, Aberer K (2013) Effective consumption scheduling for demand-side management in the smart grid using non-uniform participation rate. In: 2013 Sustainable Internet and ICT for Sustainability (SustainIT), IEEE, pp 1–8
2013
Earlier work this paper cites.
Baier H, Winands MH (2014) MCTS-minimax hybrids. IEEE Transactions on Computational Intelligence and AI in Games 7(2):167–179
2014
Earlier work this paper cites.
Barriga NA, Stanescu M, Buro M (2014) Parallel UCT search on GPUs. In: 2014 IEEE Conference on Computational Intelligence and Games, IEEE, pp 1–7
2014
Earlier work this paper cites.
Caceres-Cruz J, Arias P, Guimarans D, Riera D, Juan AA (2014) Rich Vehicle Routing Problem: Survey. ACM Computing Surveys (CSUR) 47(2):1–28
2014
Earlier work this paper cites.
Feldman Z, Domshlak C (2014) Simple regret optimization in online planning for Markov decision processes. Journal of Artificial Intelligence Research 51:165–205
2014
Cited alongside, same era.
Genesereth M, Thielscher M (2014) General Game Playing. Synthesis Lectures on Artificial Intelligence and Machine Learning 8(2):1–229
2014
Cited alongside, same era.
Graf T, Platzner M (2014) Common fate graph patterns in Monte Carlo Tree Search for computer Go. In: 2014 IEEE Conference on Computational Intelligence and Games, IEEE, pp 1–8
2014
Cited alongside, same era.
Guo X, Singh S, Lee H, Lewis RL, Wang X (2014) Deep learning for real-time Atari game play using offline Monte Carlo Tree Search planning. In: Advances in neural information processing systems, pp 3338–3346
2014
Cited alongside, same era.
Anthony T, Tian Z, Barber D (2017) Thinking fast and slow with deep learning and tree search. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in Neural Information Processing Systems, Curran Associates, Inc., vol 30
2017
Later among the works it cites.
Barriga NA, Stanescu M, Buro M (2017) Combining strategic learning and tactical search in Real-Time Strategy Games. In: Proceedings, The Thirteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-17)
2017
Later among the works it cites.
Bravi I, Khalifa A, Holmgård C, Togelius J (2017) Evolving Game-Specific UCB Alternatives for General Video Game Playing. In: European Conference on the Applications of Evolutionary Computation, Springer, pp 393–406
2017
Later among the works it cites.
Demediuk S, Tamassia M, Raffe WL, Zambetta F, Li X, Mueller F (2017) Monte Carlo Tree Search based algorithms for dynamic difficulty adjustment. In: 2017 IEEE conference on computational intelligence and games (CIG), IEEE, pp 53–59
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2014
Cited alongside, same era.
Justesen N, Tillman B, Togelius J, Risi S (2014) Script- and cluster-based UCT for Starcraft. In: 2014 IEEE Conference on Computational Intelligence and Games, pp 1–8
2014
Cited alongside, same era.
Lanctot M, Winands MH, Pepels T, Sturtevant NR (2014) Monte Carlo Tree Search with heuristic evaluations using implicit minimax backups. In: 2014 IEEE Conference on Computational Intelligence and Games, IEEE, pp 1–8
2014
Cited alongside, same era.
Lucas SM, Samothrakis S, Perez D (2014) Fast Evolutionary Adaptation for Monte Carlo Tree Search. In: European Conference on the Applications of Evolutionary Computation, Springer, pp 349–360
2014
Cited alongside, same era.
Pepels T, Winands MH, Lanctot M (2014) Real-time Monte Carlo Tree Search in MS Pac-Man. IEEE Transactions on Computational Intelligence and AI in games 6(3):245–257
2014
Cited alongside, same era.
Perez D, Samothrakis S, Lucas S (2014) Knowledge-Based Fast Evolutionary MCTS for General Video Game Playing. In: 2014 IEEE Conference on Computational Intelligence and Games, IEEE, pp 1–8
2014
Cited alongside, same era.
Perez-Liebana D, Samothrakis S, Togelius J, Schaul T, Lucas SM, Couëtoux A, Lee J, Lim CU, Thompson T (2016) The 2014 General Video Game Playing competition. IEEE Transactions on Computational Intelligence and AI in Games 8(3):229–243
2014
Cited alongside, same era.
Plaat A (2014) Mtd (f), a minimax algorithm faster than negascout. arXiv preprint arXiv:14041511
2014
Cited alongside, same era.
2017
Later among the works it cites.
Gaina RD, Lucas SM, Perez-Liebana D (2017b) Rolling Horizon Evolution Enhancements in General Video Game Playing. In: 2017 IEEE Conference on Computational Intelligence and Games (CIG), IEEE, pp 88–95
2017
Later among the works it cites.
Gao C, Hayward R, Müller M (2017) Move prediction using deep convolutional neural networks in Hex. IEEE Transactions on Games 10(4):336–343
2017
Later among the works it cites.
Guerrero-Romero C, Louis A, Perez-Liebana D (2017) Beyond playing to win: Diversifying heuristics for GVGAI. In: 2017 IEEE Conference on Computational Intelligence and Games (CIG), IEEE, pp 118–125
2017
Later among the works it cites.
Ilhan E, Etaner-Uyar AŞ (2017) Monte Carlo Tree Search with temporal-difference learning for General Video Game Playing. In: 2017 IEEE Conference on Computational Intelligence and Games (CIG), IEEE, pp 317–324
2017
Later among the works it cites.
Joppen T, Moneke MU, Schröder N, Wirth C, Fürnkranz J (2017) Informed hybrid game tree search for General Video Game Playing. IEEE Transactions on Games 10(1):78–90
2017
Later among the works it cites.
Kim MJ, Kim KJ (2017) Opponent modeling based on action table for MCTS-based fighting game AI. In: 2017 IEEE conference on computational intelligence and games (CIG), IEEE, pp 178–180
2017
Later among the works it cites.
Maia LF, Viana W, Trinta F (2017) Using Monte Carlo Tree Search and Google Maps to improve game balancing in location-based games. In: 2017 IEEE Conference on Computational Intelligence and Games (CIG), IEEE, pp 215–222
2017
Later among the works it cites.
Mańdziuk J, Świechowski M (2017) UCT in capacitated vehicle routing problem with traffic jams. Information Sciences 406:42–56
2017
Later among the works it cites.
Santos A, Santos PA, Melo FS (2017) Monte Carlo Tree Search experiments in Hearthstone. In: 2017 IEEE Conference on Computational Intelligence and Games (CIG), pp 272–279
2017
Later among the works it cites.
Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, Hubert T, Baker L, Lai M, Bolton A, et al. (2017) Mastering the game of Go without human knowledge. nature 550(7676):354–359
2017
Later among the works it cites.
Uriarte A, Ontañón S (2017) Single believe state generation for partially observable Real-Time Strategy Games. In: 2017 IEEE Conference on Computational Intelligence and Games (CIG), pp 296–303
2017
Later among the works it cites.
Zhang S, Buro M (2017) Improving Hearthstone AI by learning high-level rollout policies and bucketing chance node events. In: 2017 IEEE Conference on Computational Intelligence and Games (CIG), pp 309–316
2017
Later among the works it cites.
Awais M, Mohammadi HG, Platzner M (2018) An MCTS-based framework for synthesis of approximate circuits. In: 2018 IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC), IEEE, pp 219–224
2018
Later among the works it cites.
Baier H, Cowling PI (2018) Evolutionary MCTS for multi-action adversarial games. In: 2018 IEEE Conference on Computational Intelligence and Games (CIG), IEEE, pp 1–8
2018
Later among the works it cites.
Baier H, Sattaur A, Powley EJ, Devlin S, Rollason J, Cowling PI (2018) Emulating human play in a leading mobile card game. IEEE Transactions on Games 11(4):386–395
2018
Later among the works it cites.
Chang NY, Chen CH, Lin SS, Nair S (2018) The Big Win Strategy on Multi-Value Network: An Improvement over AlphaZero Approach for 6x6 Othello. In: Proceedings of the 2018 International Conference on Machine Learning and Machine Intelligence, pp 78–81
2018
Later among the works it cites.
Clary P, Morais P, Fern A, Hurst JW (2018) Monte-Carlo Planning for Agile Legged Locomotion. In: ICAPS, pp 446–450
2018
Later among the works it cites.
Di Palma S, Lanzi PL (2018) Traditional wisdom and Monte Carlo Tree Search face-to-face in the card game Scopone. IEEE Transactions on Games 10(3):317–332
2018
Later among the works it cites.
Gao C, Müller M, Hayward R (2018) Three-Head Neural Network Architecture for Monte Carlo Tree Search. In: Twenty-Seventh International Joint Conference on Artificial Intelligence, pp 3762–3768
2018
Later among the works it cites.
Gao C, Takada K, Hayward R (2019) Hex 2018: MoHex3HNN over DeepEzo. J Int Comput Games Assoc 41(1):39–42
2018
Later among the works it cites.
Gedda M, Lagerkvist MZ, Butler M (2018) Monte Carlo methods for the game Kingdomino. In: 2018 IEEE Conference on Computational Intelligence and Games (CIG), IEEE, pp 1–8
2018
Later among the works it cites.
Gholami S, Mc Carthy S, Dilkina B, Plumptre AJ, Tambe M, Driciru M, Wanyama F, Rwetsiba A, Nsubaga M, Mabonga J, et al. (2018) Adversary models account for imperfect crime data: Forecasting and planning against real-world poachers. In: Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems, pp 823–831
2018
Later among the works it cites.
Ihara H, Imai S, Oyama S, Kurihara M (2018) Implementation and evaluation of information set Monte Carlo Tree Search for Pokémon. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp 2182–2187
2018
Later among the works it cites.
Ishihara M, Ito S, Ishii R, Harada T, Thawonmas R (2018) Monte Carlo Tree Search for implementation of dynamic difficulty adjustment fighting game AIs having believable behaviors. In: 2018 IEEE Conference on Computational Intelligence and Games (CIG), IEEE, pp 1–8
2018
Later among the works it cites.
Keehl O, Smith AM (2018) Monster Carlo: an MCTS-based framework for machine playtesting Unity games. In: 2018 IEEE Conference on Computational Intelligence and Games (CIG), IEEE, pp 1–8
2018
Later among the works it cites.
Kurzer K, Zhou C, Zöllner JM (2018) Decentralized cooperative planning for automated vehicles with hierarchical Monte Carlo Tree Search. In: 2018 IEEE Intelligent Vehicles Symposium (IV), IEEE, pp 529–536
2018
Later among the works it cites.
Mańdziuk J (2018) MCTS/UCT in solving real-life problems. In: Advances in Data Analysis with Computational Intelligence Methods - Dedicated to Professor Jacek Żurada, Springer, Studies in Computational Intelligence, vol 738, pp 277–292
2018
Later among the works it cites.
Mirsoleimani SA, van den Herik HJ, Plaat A, Vermaseren J (2018) A Lock-free Algorithm for Parallel MCTS. In: ICAART - 10th International Conference on Agents and Artificial Intelligence, pp 589–598
2018
Later among the works it cites.
Moraes RO, Marino JR, Lelis LH, Nascimento MA (2018) Action abstractions for combinatorial multi-armed bandit tree search. In: AIIDE, pp 74–80
2018
Later among the works it cites.
Pinto IP, Coutinho LR (2018) Hierarchical reinforcement learning with Monte Carlo Tree Search in computer fighting game. IEEE transactions on games 11(3):290–295
2018
Later among the works it cites.
Segler MH, Preuss M, Waller MP (2018) Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555(7698):604–610
2018
Later among the works it cites.
Silver D, Hubert T, Schrittwieser J, Antonoglou I, Lai M, Guez A, Lanctot M, Sifre L, Kumaran D, Graepel T, Lillicrap T, Simonyan K, Hassabis D (2018) A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science 362(6419):1140–1144, URL https://science.sciencemag.org/content/362/6419/1140 , https://science.sciencemag.org/content/362/6419/1140.full.pdf
2018
Later among the works it cites.
Sironi CF, Liu J, Winands MH (2018) Self-adaptive Monte Carlo Tree Search in General Game Playing. IEEE Transactions on Games
2018
Later among the works it cites.
Swiechowski M, Slęzak D (2018) Granular games in real-time environment. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, pp 462–469
2018
Later among the works it cites.
Świechowski M, Tajmajer T, Janusz A (2018) Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms. In: 2018 IEEE Conference on Computational Intelligence and Games (CIG), pp 1–8
2018
Later among the works it cites.
Waledzik K, Mandziuk J (2018) Applying hybrid Monte Carlo Tree Search methods to risk-aware project scheduling problem. Information Sciences 460-461:450–468
2018
Later among the works it cites.
Wu TR, Wu IC, Chen GW, Wei Th, Wu HC, Lai TY, Lan LC (2018) Multilabeled value networks for computer Go. IEEE Transactions on Games 10(4):378–389
2018
Later among the works it cites.
Yang Z, Ontañón S (2018) Learning map-independent evaluation functions for Real-Time Strategy Games. In: 2018 IEEE Conference on Computational Intelligence and Games (CIG), IEEE, pp 1–7
2018
Later among the works it cites.
Zhou H, Gong Y, Mugrai L, Khalifa A, Nealen A, Togelius J (2018) A hybrid search agent in Pommerman. In: Proceedings of the 13th International Conference on the Foundations of Digital Games, pp 1–4
2018
Later among the works it cites.
Ba S, Hiraoka T, Onishi T, Nakata T, Tsuruoka Y (2019) Monte Carlo Tree Search with variable simulation periods for continuously running tasks. In: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), IEEE, pp 416–423
2019
Later among the works it cites.
Best G, Cliff OM, Patten T, Mettu RR, Fitch R (2019) Dec-MCTS: Decentralized planning for multi-robot active perception. The International Journal of Robotics Research 38(2-3):316–337
2019
Later among the works it cites.
Choe JSB, Kim J (2019) Enhancing Monte Carlo Tree Search for playing Hearthstone. In: 2019 IEEE Conference on Games (CoG), pp 1–7
2019
Later among the works it cites.
Gabor T, Peter J, Phan T, Meyer C, Linnhoff-Popien C (2019) Subgoal-based temporal abstraction in Monte Carlo Tree Search. In: IJCAI, pp 5562–5568
2019
Later among the works it cites.
Gaymann A, Montomoli F (2019) Deep neural network and Monte Carlo Tree Search applied to fluid-Structure topology optimization. Scientific reports 9(1):1–16
2019
Later among the works it cites.
Goodman J (2019) Re-determinizing MCTS in Hanabi. In: 2019 IEEE Conference on Games (CoG), IEEE, pp 1–8
2019
Later among the works it cites.
Keehl O, Smith AM (2019) Monster Carlo 2: Integrating learning and tree search for machine playtesting. In: 2019 IEEE Conference on Games (CoG), IEEE, pp 1–8
2019
Later among the works it cites.
Mańdziuk J (2019) New shades of the vehicle routing problem: Emerging problem formulations and computational intelligence solution methods. IEEE Transactions on Emerging Topics in Computational Intelligence 3(3):230–244
2019
Later among the works it cites.
Sharma A, Harrison J, Tsao M, Pavone M (2019) Robust and adaptive planning under model uncertainty. In: Proceedings of the International Conference on Automated Planning and Scheduling, vol 29, pp 410–418
2019
Later among the works it cites.
Sironi CF, Winands MH (2019) Comparing randomization strategies for search-control parameters in Monte Carlo Tree Search. In: 2019 IEEE Conference on Games (CoG), IEEE, pp 1–8
2019
Later among the works it cites.
Soemers DJ, Piette E, Stephenson M, Browne C (2019) Learning Policies from Self-Play with Policy Gradients and MCTS Value Estimates. In: 2019 IEEE Conference on Games (CoG), IEEE, pp 1–8
2019
Later among the works it cites.
Takada K, Iizuka H, Yamamoto M (2019) Reinforcement learning to create value and policy functions using minimax tree search in hex. IEEE Transactions on Games 12(1):63–73
2019
Later among the works it cites.
Zook A, Harrison B, Riedl MO (2019) Monte Carlo Tree Search for simulation-based strategy analysis. arXiv preprint arXiv:190801423
2019
Later among the works it cites.
Baier H, Kaisers M (2020) Guiding Multiplayer MCTS by Focusing on Yourself. In: 2020 IEEE Conference on Games (CoG), IEEE, pp 550–557
2020
Later among the works it cites.
Bondi E, Oh H, Xu H, Fang F, Dilkina B, Tambe M (2020) To signal or not to signal: Exploiting uncertain real-time information in signaling games for security and sustainability. In: Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, pp 1369–1377
2020
Later among the works it cites.
Gaina RD, Perez-Liebana D, Lucas SM, Sironi CF, Winands MH (2020) Self-adaptive rolling horizon evolutionary algorithms for general video game playing. In: 2020 IEEE Conference on Games (CoG), IEEE, pp 367–374
2020
Later among the works it cites.
Goodman J, Lucas S (2020) Does it matter how well I know what you’re thinking? Opponent Modelling in an RTS game. In: 2020 IEEE Congress on Evolutionary Computation (CEC), IEEE, pp 1–8
2020
Later among the works it cites.
IEEE-CoG (2020) Call for Competitions, IEEE Conference on Games. URL https://ieee-cog.org/2020/competitions
2020
Later among the works it cites.
Jia J, Chen J, Wang X (2020) Ultra-high reliable optimization based on Monte Carlo Tree Search over Nakagami-m Fading. Applied Soft Computing p 106244
2020
Later among the works it cites.
Karwowski J, Mańdziuk J, Zychowski A (2020) Anchoring theory in Sequential Stackelberg Games. In: Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS ‘20, Auckland, New Zealand, International Foundation for Autonomous Agents and Multiagent Systems, pp 1881–1883
2020
Later among the works it cites.
Neto T, Constantino M, Martins I, Pedroso JP (2020) A multi-objective Monte Carlo Tree Search for forest harvest scheduling. European Journal of Operational Research 282(3):1115–1126
2020
Later among the works it cites.
Painter M, Lacerda B, Hawes N (2020) Convex Hull Monte-Carlo Tree-Search. In: Proceedings of the International Conference on Automated Planning and Scheduling, vol 30, pp 217–225
2020
Later among the works it cites.
Patra S, Mason J, Kumar A, Ghallab M, Traverso P, Nau D (2020) Integrating Acting, Planning, and Learning in Hierarchical Operational Models. In: Proceedings of the International Conference on Automated Planning and Scheduling, vol 30, pp 478–487
2020
Later among the works it cites.
Preuss M, Risi S (2020) A Games Industry Perspective on Recent Game AI Developments. KI-Künstliche Intelligenz 34(1):81–83
2020
Later among the works it cites.
Shi F, Soman RK, Han J, Whyte JK (2020) Addressing adjacency constraints in rectangular floor plans using Monte Carlo Tree Search. Automation in Construction 115:103187
2020
Later among the works it cites.
Świechowski M (2020) Game AI Competitions: Motivation for the Imitation Game-Playing Competition. In: 2020 Federated Conference on Computer Science and Information Systems (FedCSIS), IEEE, vol 21, pp 155–160
2020
Later among the works it cites.
Wang K, Perrault A, Mate A, Tambe M (2020) Scalable game-focused learning of adversary models: Data-to-decisions in network security games. In: Seghrouchni AEF, Sukthankar G, An B, Yorke-Smith N (eds) Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS ‘20, Auckland, New Zealand, May 9-13, 2020, International Foundation for Autonomous Agents and Multiagent Systems, pp 1449–1457
2020
Later among the works it cites.
Yang B, Wang L, Lu H, Yang Y (2020) Learning the Game of Go by Scalable Network without Prior Knowledge of Komi. IEEE Transactions on Games
2020
Later among the works it cites.
Gaina RD, Devlin S, Lucas SM, Perez D (2021) Rolling Horizon Evolutionary Algorithms for General Video Game Playing. IEEE Transactions on Games
2021
Closest in time.
Godlewski K, Sawicki B (2021) Optimisation of MCTS Player for The Lord of the Rings: The Card Game. Bulletin of the Polish Academy of Science: Technical Sciences 69
2021
Closest in time.
Hu Z, Tu J, Li B (2019) Spear: Optimized Dependency-Aware Task Scheduling with Deep Reinforcement Learning. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp 2037–2046
2046
Closest in time.
Karwowski J, Mańdziuk J (2019b) Stackelberg Equilibrium Approximation in general-sum extensive-form games with double-oracle sampling method. In: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS ‘19, Montreal, QC, Canada, International Foundation for Autonomous Agents and Multiagent Systems, pp 2045–2047
2047
Closest in time.
Karwowski J, Mańdziuk J (2020) Double-oracle sampling method for Stackelberg Equilibrium Approximation in general-sum extensive-form games. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, New York, NY, USA, AAAI Press, pp 2054–2061
2061
Closest in time.