Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Presence and absence of pathology on game trees
Advances in computer chess
The effect of mobility on minimaxing of game trees with random leaf values
Artificial Intelligence
Quality of decision versus depth of search on game trees
Quality of decision versus depth of search on game trees
Pessimistic Heuristics Beat Optimistic Ones in Real-Time Search
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Properties of forward pruning in game-tree search
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Search versus knowledge revisited again
CG'06 Proceedings of the 5th international conference on Computers and games
When is it better not to look ahead?
Artificial Intelligence
Independent-valued minimax: Pathological or beneficial?
Theoretical Computer Science
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This article presents the results of experiments designed to gain insight into the effect of the minimax algorithm on the error of a heuristic evaluation function. Two types of effect of minimax are considered: (a) evaluation accuracy (Are the minimax backed-up values more accurate than the heuristic values themselves?), and (b) decision accuracy (Are moves played by deeper minimax search better than those by shallower search?). The experiments were performed in the King-Rook-King (KRK) chess endgame and in randomly generated game trees. The results show that, counter-intuitively, evaluation accuracy may decline with search depth, whereas at the same time decision accuracy improves with depth. In the article, this is explained by the fact that minimax in combination with a noisy evaluation function introduces a bias into the backed-up evaluations, which masks the evaluation effectiveness of minimax, but this bias still permits decision accuracy to improve with depth. This observed behaviour of minimax in the KRK endgame is discussed in the light of previous studies of pathology in minimax. It is shown that explaining the behaviour of minimax in an actual chess endgame in terms of previously known results requires special care.