Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Experiments on alternatives to minimax
International Journal of Parallel Programming
Comparison of the minimax and product back-up rules in a variety of games
Search in Artificial Intelligence
Minimax Search Algorithms With and Without Aspiration Windows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Benefits of using multivalued functions for minimaxing
Artificial Intelligence
Experiments With a Multipurpose, Theorem-Proving Heuristic Program
Journal of the ACM (JACM)
Searching to variable depth in computer chess
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Pathology on game trees revisited, and an alternative to minimaxing
Artificial Intelligence
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The minimaxing approach to backing-up heuristic values has been very successful, e.g., in computer chess for making move decisions on the world-champion level, by employing very deep searches and effective pruning algorithms. From a theoretical point of view, however, it is not yet clear whether minimaxing or product propagation is better in terms of decision quality without deep searches. We present a systematic analysis of game trees with depth 2, where a single application of the competing back-up rules each (in a given branch from the root of the search tree) reveals their pure decision quality. Interestingly, product propagation tends to make better decisions per se more frequently, under realistic assumptions modeled after real game-playing programs. So, its decision quality may still make it a viable alternative for game trees where only shallow searches are affordable.