Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Combining online and offline knowledge in UCT
Proceedings of the 24th international conference on Machine learning
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
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Efficient selectivity and backup operators in Monte-Carlo tree search
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
Consistency modifications for automatically tuned Monte-Carlo tree search
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Scalability and parallelization of Monte-Carlo tree search
CG'10 Proceedings of the 7th international conference on Computers and games
Biasing Monte-Carlo simulations through RAVE values
CG'10 Proceedings of the 7th international conference on Computers and games
Computational experiments with the RAVE heuristic
CG'10 Proceedings of the 7th international conference on Computers and games
Improving Monte-Carlo tree search in Havannah
CG'10 Proceedings of the 7th international conference on Computers and games
Monte-Carlo opening books for amazons
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
Revisiting Monte-Carlo tree search on a normal form game: NoGo
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Multiple overlapping tiles for contextual monte carlo tree search
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
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Monte-Carlo Tree Search and Upper Confidence Bounds provided huge improvements in computer-Go. In this paper, we test the generality of the approach by experimenting on the game, Havannah, which is known for being especially difficult for computers. We show that the same results hold, with slight differences related to the absence of clearly known patterns for the game of Havannah, in spite of the fact that Havannah is more related to connection games like Hex than to territory games like Go.