Affiliations: Computing Science Department, University of Alberta, Edmonton, Canada T6G 2H1
Note: [1] Much of this article is a revision of material condensed from an entry entitled “Computer Chess Methods,” prepared for the Encyclopedia of Artificial Intelligence, S. Shapiro (editor), to be published by John Wiley & Sons in 1987. The transposition table pseudo code of Figure 7 is similar to that in another paper: “Parallel Search of Strongly Ordered Game Trees,” T.A. Marsland and M. Campbell, ACM Computing Surveys, Vol 14, No.4, copyright 1982, Association for Computing Machinery Inc., and is reused here by permission.
Abstract: Chess programs have three major components: move generation, search, and evaluation. All components are important, although evaluation with its quiescence analysis is the part which makes each program’s play unique. The speed of a chess program is a function of its move generation cost, the complexity of the position under study and the brevity of its evaluation. More important, however, is the quality of the mechanisms used to discontinue (prune) search of unprofitable continuations. The most reliable pruning method in popular use is the robust alpha-beta algorithm, and its many supporting aids. These essential parts of game-tree searching and pruning are reviewed’ here, and the performance of refinements, such as aspiration and principal variation search, and aids like transposition and history tables are compared.
DOI: 10.3233/ICG-1986-9102
Journal: ICGA Journal, vol. 9, no. 1, pp. 3-19, 1986
