*-Minimax Performance in Backgammon (original) (raw)

Lecture Notes in Computer Science, 2006

Abstract

This paper presents the rst performance results for Bal- lard's *-Minimax algorithms applied to a real{world domain: backgam- mon. It is shown that with eectiv e move ordering and probing the Star2 algorithm considerably outperforms Expectimax. Star2 allows strong backgammon programs to conduct depth 5 full-width searches (up from 3) under tournament conditions on regular hardware without using risky forward pruning techniques. We also present empirical evidence that with today's sophisticated evaluation functions good checker play in backgam- mon does not require deep searches.

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