Nested Monte-Carlo Search
Cazenave, Tristan (2009), Nested Monte-Carlo Search, IJCAI 2009 International Joint Conferences on Artificial Intelligence, 2009-07, Pasadena, États-Unis
TypeCommunication / Conférence
Conference titleIJCAI 2009 International Joint Conferences on Artificial Intelligence
MetadataShow full item record
Abstract (EN)Many problems have a huge state space and no good heuristic to order moves so as to guide the search toward the best positions. Random games can be used to score positions and evaluate their interest. Random games can also be improved using random games to choose a move to try at each step of a game. Nested Monte-Carlo Search addresses the problem of guiding the search toward better states when there is no available heuristic. It uses nested levels of random games in order to guide the search. The algorithm is studied theoretically on simple abstract problems and applied successfully to three different games: Morpion Solitaire, SameGame and 16x16 Sudoku.
Subjects / KeywordsRandom Games
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