Forward estimation for game-tree search
- Univ. of Southern California, Marina del Rey, CA (United States)
It is known that bounds on the minimax values of nodes in a game tree can be used to reduce the computational complexity of minimax search for two-player games. We describe a very simple method to estimate bounds on the minimax values of interior nodes of a game tree, and use the bounds to improve minimax search. The new algorithm, called forward estimation, does not require additional domain knowledge other than a static node evaluation function, and has small constant overhead per node expansion. We also propose a variation of forward estimation, which provides a tradeoff between computational complexity and decision quality. Our experimental results show that forward estimation outperforms alpha-beta pruning on random game trees and the game of Othello.
- OSTI ID:
- 430662
- Report Number(s):
- CONF-960876-; TRN: 96:006521-0037
- Resource Relation:
- Conference: 13. National conference on artifical intelligence and the 8. Innovative applications of artificial intelligence conference, Portland, OR (United States), 4-8 Aug 1996; Other Information: PBD: 1996; Related Information: Is Part Of Proceedings of the thirteenth national conference on artificial intelligence and the eighth innovative applications of artificial intelligence conference. Volume 1 and 2; PB: 1626 p.
- Country of Publication:
- United States
- Language:
- English
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