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• W1601222786 abstract "We propose /spl alpha/-/spl beta/-evaluation functions that can be used in game-playing programs as a substitute for the traditional static evaluation functions without loss of functionality. The main advantage of an /spl alpha/-/spl beta/-evaluation function is that it can be implemented with a much lower time complexity than the traditional counterpart and so provides a significant speedup for the evaluation of any game position which eventually results in better play. We describe an implementation of the /spl alpha/-/spl beta/-evaluation function using a modification of the classical classification and regression trees and show that a typical call to this function involves the computation of only a small subset of all features that may be used to describe a game position. We show that an iterative bootstrap process con be used to learn /spl alpha/-/spl beta/-evaluation functions efficiently and describe some of the experience we made with this new approach applied to a game called Malawi. >" @default.
• W1601222786 created "2016-06-24" @default.
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• W1601222786 date "2002-12-30" @default.
• W1601222786 modified "2023-09-26" @default.
• W1601222786 title "Bootstrap learning of α-β-evaluation functions" @default.
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• W1601222786 doi "https://doi.org/10.1109/icci.1993.315347" @default.
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