Game-tree Search Algorithm based on Realization Probability

In games like chess, the node-expansion strategy significantly affects the performance of a gameplaying program. In this article we propose a new game-tree search algorithm that uses the realization probabilities of nodes for deciding upon the range of the search. The realization probability of a node represents the probability that the moves leading to the node will actually be played. Our algorithm expands nodes as long as the realization probability of a node is greater than the threshold. Therefore, it spends little computational resource on unrealistic moves, resulting in a more effective search. We have implemented this algorithm in a Shogi-playing program. Experimental results show that the proposed algorithm achieves state-of-the-art performance on a standard test suite for computer Shogi. Moreover, its performance gain is equivalent to a speed-up of more than two.