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Nested Monte-Carlo Search

Cazenave, Tristan (2009), Nested Monte-Carlo Search, IJCAI 2009 International Joint Conferences on Artificial Intelligence, 2009-07, Pasadena, États-Unis

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IJCAI09-083.pdf (1.038Mb)
Type
Communication / Conférence
Date
2009
Conference title
IJCAI 2009 International Joint Conferences on Artificial Intelligence
Conference date
2009-07
Conference city
Pasadena
Conference country
États-Unis
Pages
6
Metadata
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Author(s)
Cazenave, Tristan
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 / Keywords
Random Games

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