Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Generalization of alpha-beta SSS* search procedures
Artificial Intelligence
Game tree searching by min/max approximation
Artificial Intelligence
Conspiracy numbers for min-max search
Artificial Intelligence
The development of a world class Othello program
Artificial Intelligence - Special issue on computer chess
Singular extensions: adding selectivity to brute-force searching
Artificial Intelligence - Special issue on computer chess
Minimax Search Algorithms With and Without Aspiration Windows
IEEE Transactions on Pattern Analysis and Machine Intelligence
An analysis of forward pruning
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Performance of linear-space search algorithms
Artificial Intelligence
Artificial Intelligence
ISAAC '92 Proceedings of the Third International Symposium on Algorithms and Computation
On optimal game-tree search using rational meta-reasoning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
An expected-cost analysis of backtracking and non-backtracking algorithms
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Searching for an optimal path in a tree with random costs
Artificial Intelligence
| Hi-index | 0.00 |
It is known that bounds on the mmnnax 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.