Nested Monte Carlo Search for Two-Player Games
Cazenave, Tristan; Saffidine, Abdallah; Schofield, Michael John; Thielscher, Michael (2016), Nested Monte Carlo Search for Two-Player Games, in Dale Schuurmans, Michael Wellman, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), AAAI Press : Palo Alto (USA), p. 687-693
TypeCommunication / Conférence
Book titleProceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16)
Book authorDale Schuurmans, Michael Wellman
MetadataShow full item record
Abstract (EN)The use of the Monte Carlo playouts as an evaluation function has proved to be a viable, general technique for searching intractable game spaces. This facilitate the use of statistical techniques like Monte Carlo Tree Search (MCTS), but is also known to require significant processing overhead. Weseek to improve the quality of information extracted from the Monte Carlo playout in three ways. Firstly, by nesting the evaluation function inside another evaluation function; secondly, by measuring and utilising the depth of the playout; and thirdly, by incorporating pruning strategies that eliminateunnecessary searches and avoid traps. Our experimental data, obtained on a variety of two-player games from past General Game Playing (GGP) competitions and others, demonstrate the usefulness of these techniques in a Nested Player when pitted against a standard, optimised UCT player.
Subjects / KeywordsMonte Carlo Tree Search
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