Minimax Performance in Backgammon
This paper presents the first performance results for Ballard’s *-Minimax algorithms applied to a real–world domain: backgammon. It is shown that with effective move ordering and probing the Star2 algorithm considerably outperforms Expectimax. Star2 allows strong backgammon programs to conduct depth...
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Format: | Buchkapitel |
Sprache: | eng |
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Zusammenfassung: | This paper presents the first performance results for Ballard’s *-Minimax algorithms applied to a real–world domain: backgammon. It is shown that with effective 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 backgammon does not require deep searches. |
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ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/11674399_4 |