Improving heuristic mini-max search by supervised learning

This article surveys three techniques for enhancing heuristic game-tree search pioneered in the author's Othello program Logistello, which dominated the computer Othello scene for several years and won against the human World-champion 6-0 in 1997. First, a generalized linear evaluation model GLEM is described that combines conjunctions of Boolean features linearly. This approach allows an automatic, data driven exploration of the feature space. Combined with e cient least squares weight tting, GLEM greatly eases the programmer's task of nding significant features and assigning weights to them. Second, the selective search heuristic ProbCut and its enhancements are discussed. Based on evaluation correlations ProbCut can prune probably irrelevant sub-trees with a prescribed con dence. Tournament results indicate a considerable playing strength improvement compared to full-width - search. Third, an opening book framework is presented that enables programs to improve upon previo...

• Type: Journal
• Year: 2002
• Where: AI
• Authors: Michael Buro
• Keywords: AI 2002, Artificial Intelligence, Game-tree Search, Heuristic Game-tree Search, Othello Program Logistello