Combining UCT and Nested Monte Carlo Search for Single-Player General Game Playing

Monte Carlo tree search (MCTS) has been recently very successful for game playing, particularly for games where the evaluation of a state is difficult to compute, such as Go or General Games. We compare nested Monte Carlo (NMC) search, upper confidence bounds for trees (UCT-T), UCT with transpositio...

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Veröffentlicht in:IEEE transactions on computational intelligence and AI in games. Jg. 2; H. 4; S. 271 - 277
Hauptverfasser: Méhat, Jean, Cazenave, Tristan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.12.2010
Schlagworte:
Algorithm design and analysis
Artificial intelligence
Classification algorithms
Competition
Computer simulation
Decision trees
Games
General Game Playing (GPP)
Monte Carlo methods
nested Monte-Carlo search
Searching
single-player games
Tables
Trees
upper confidence bounds for trees (UCT)
ISSN:1943-068X, 1943-0698
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Zusammenfassung:Monte Carlo tree search (MCTS) has been recently very successful for game playing, particularly for games where the evaluation of a state is difficult to compute, such as Go or General Games. We compare nested Monte Carlo (NMC) search, upper confidence bounds for trees (UCT-T), UCT with transposition tables (UCT+T), and a simple combination of NMC and UCT+T (MAX) on single-player games of the past General Game Playing (GGP) competitions. We show that transposition tables improve UCT and that MAX is the best of these four algorithms. Using UCT+T, the program Ary won the 2009 GGP competition. MAX and NMC are slight improvements over this 2009 version.
Bibliographie:ObjectType-Article-2
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ISSN:1943-068X
1943-0698
DOI:10.1109/TCIAIG.2010.2088123