Multiplayer games: algorithms and approaches
Multiplayer games: algorithms and approaches
Robust game play against unknown opponents
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Prob-Maxn: playing N-player games with opponent models
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
GIB: imperfect information in a computationally challenging game
Journal of Artificial Intelligence Research
Last-branch and speculative pruning algorithms for max
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Feature construction for reinforcement learning in hearts
CG'06 Proceedings of the 5th international conference on Computers and games
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Current challenges in multi-player game search
CG'04 Proceedings of the 4th international conference on Computers and Games
Enhancements for multi-player Monte-Carlo tree search
CG'10 Proceedings of the 7th international conference on Computers and games
Monte-Carlo tree search and rapid action value estimation in computer Go
Artificial Intelligence
Learning to win by reading manuals in a Monte-Carlo framework
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Computing approximate Nash Equilibria and robust best-responses using sampling
Journal of Artificial Intelligence Research
Non-linear Monte-Carlo search in civilization II
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Single-player Monte-Carlo tree search for SameGame
Knowledge-Based Systems
Learning to win by reading manuals in a monte-carlo framework
Journal of Artificial Intelligence Research
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The UCT algorithm has been exceedingly popular for Go, a two-player game, significantly increasing the playing strength of Go programs in a very short time. This paper provides an analysis of the UCT algorithm in multi-player games, showing that UCT, when run in a multi-player game, is computing a mixed-strategy equilibrium, as opposed to maxn, which computes a pure-strategy equilibrium. We analyze the performance of UCT in several known domains and show that it performs as well or better than existing algorithms.