Nested Monte-Carlo Search
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 456-460.
Abstract
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.Tristan Cazenave
@inproceedings{ijcai2009p83,
title = {Nested Monte-Carlo Search},
author = {Cazenave, Tristan},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {456--460},
year = {2009},
url = {https://ijcai.org/proceedings/2009/83},
}
TY - CPAPER TI - Nested Monte-Carlo Search AU - Cazenave, Tristan T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 456 EP - 460 UR - https://ijcai.org/proceedings/2009/83 ER -Copied
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