Expected-Outcome: A General Model of Static Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A study of decision error in selective game tree search
Information Sciences: an International Journal - Heuristic Search and Computer Game Playing
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
GIB: steps toward an expert-level bridge-playing program
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Associating domain-dependent knowledge and Monte Carlo approaches within a Go program
Information Sciences: an International Journal
Efficient selectivity and backup operators in Monte-Carlo tree search
CG'06 Proceedings of the 5th international conference on Computers and games
ACG'05 Proceedings of the 11th international conference on Advances in Computer Games
Solving probabilistic combinatorial games
ACG'05 Proceedings of the 11th international conference on Advances in Computer Games
Associating shallow and selective global tree search with monte carlo for 9 × 9 go
CG'04 Proceedings of the 4th international conference on Computers and Games
CG'04 Proceedings of the 4th international conference on Computers and Games
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In games, Monte-Carlo simulations can be used as an evaluation function for Alpha-Beta search. Assuming w is the width of the search tree, d its depth, and g the number of simulations at each leaf, then the total number of simulations is at least g × (2 × wd/2). In games where moves permute, we propose to replace this algorithm by a new algorithm, Virtual Global Search, that only needs g × 2d simulations for a similar number of games per leaf. The algorithm is also applicable to games where moves often but not always permute, such as Go. We specify the application for 9×9 Go.