Theory of Computing Systems
Combining online and offline knowledge in UCT
Proceedings of the 24th international conference on Machine learning
Nested Monte-Carlo Expression Discovery
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Score bounded Monte-Carlo tree search
CG'10 Proceedings of the 7th international conference on Computers and games
Monte-Carlo opening books for amazons
CG'10 Proceedings of the 7th international conference on Computers and games
Optimization of the nested Monte-Carlo algorithm on the traveling salesman problem with time windows
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
Nested rollout policy adaptation for Monte Carlo tree search
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
A real-time opponent modeling system for rush football
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Single-player Monte-Carlo tree search for SameGame
Knowledge-Based Systems
Nested Monte-Carlo Search with simulation reduction
Knowledge-Based Systems
UCD: Upper confidence bound for rooted directed acyclic graphs
Knowledge-Based Systems
Embedding monte carlo search of features in tree-based ensemble methods
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Investigating monte-carlo methods on the weak schur problem
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
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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 16×16 Sudoku.