Forward Estimation for Game-Tree Search

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.

• Type: Conference
• Year: 1996
• Where: AAAI
• Authors: Weixiong Zhang
• Keywords: AAAI 1996, Forward Estimation, Game Trees, Intelligent Agents, Minimax Values