Computer chess move-ordering schemes using move influence
Artificial Intelligence
Learning Time Allocation Using Neural Networks
CG '00 Revised Papers from the Second International Conference on Computers and Games
An effective two-level proof-number search algorithm
Theoretical Computer Science - Algorithmic combinatorial game theory
Information Sciences: an International Journal
The relative history heuristic
CG'04 Proceedings of the 4th international conference on Computers and Games
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The efficiency of alpha-beta search algorithms heavily depends on the order in which the moves are examined. This paper focuses on using neural networks to estimate the likelihood of a move being the best in a certain position. The moves considered more likely to be the best are examined first. We selected Lines of Action as a testing ground. We investigated several schemes to encode the moves in a neural network. In the experiments, the best performance was obtained by using one output unit for each possible move of the game. The results indicate that our move-ordering approach can speed up the search with 20 to 50 percent compared with one of the best current alternatives, the history heuristic.