This paper introduces Monte Carlo *-Minimax Search (MCMS), a Monte Carlo search algorithm for turned-based, stochastic, two-player, zero-sum games of perfect information. The algorithm is designed for the class of of densely stochastic games; that is, games where one would rarely expect to sample the same successor state multiple times at any particular chance node. Our approach combines sparse sampling techniques from MDP planning with classic pruning techniques developed for adversarial expectimax planning. We compare and contrast our algorithm to the traditional *-Minimax approaches, as well as MCTS enhanced with the Double Progressive Widening, on four games: Pig, EinStein W\"urfelt Nicht!, Can't Stop, and Ra. Our results show that MCMS can be competitive with enhanced MCTS variants in some domains, while consistently outperforming the equivalent classic approaches given the same amount of thinking time.
|Title of host publication||Proceedings of the 23rd International Joint Conference on Artificial Intelligence|
|Number of pages||7|
|Publication status||Published - 2013|
|Event||Twenty-third International Conference on Artificial Intelligence - Beijing, China|
Duration: 3 Aug 2013 → 9 Aug 2013
|Conference||Twenty-third International Conference on Artificial Intelligence|
|Period||3/08/13 → 9/08/13|