Applying Agent Technology to Healthcare: The GruSMA Experience
IEEE Intelligent Systems
Combining online and offline knowledge in UCT
Proceedings of the 24th international conference on Machine learning
Ontology-based intelligent decision support agent for CMMI project monitoring and control
International Journal of Approximate Reasoning
Proceedings of the 25th international conference on Machine learning
Parallel Monte-Carlo Tree Search
CG '08 Proceedings of the 6th international conference on Computers and Games
Knowledge Generation for Improving Simulations in UCT for General Game Playing
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Bandit-based optimization on graphs with application to library performance tuning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Efficient selectivity and backup operators in Monte-Carlo tree search
CG'06 Proceedings of the 5th international conference on Computers and games
Discussion forum: what computing with words means tome
IEEE Computational Intelligence Magazine
Exploring e-learning knowledge through ontological memetic agents
IEEE Computational Intelligence Magazine
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Adding expert knowledge and exploration in monte-carlo tree search
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
A lock-free multithreaded monte-carlo tree search algorithm
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
Bandit-Based genetic programming
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Let the Games Begin [President's Message]
IEEE Computational Intelligence Magazine
Genetic fuzzy markup language for game of NoGo
Knowledge-Based Systems
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Monte-Carlo Tree Search (MCTS) is a very efficient recent technology for games and planning, particularly in the high-dimensional case, when the number of time steps is moderate and when there is no natural evaluation function. Surprisingly, MCTS makes very little use of learning. In this paper, we present four techniques (ontologies, Bernstein races, Contextual Monte-Carlo and poolRave) for learning agents in Monte-Carlo Tree Search, and experiment them in difficult games and in particular, the Game of Go.