Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
The History Heuristic and Alpha-Beta Search Enhancements in Practice
IEEE Transactions on Pattern Analysis and Machine Intelligence
A generalised quiescence search algorithm
Artificial Intelligence - Special issue on computer chess
Singular extensions: adding selectivity to brute-force searching
Artificial Intelligence - Special issue on computer chess
One jump ahead: challenging human supremacy in checkers
One jump ahead: challenging human supremacy in checkers
AFIPS '67 (Fall) Proceedings of the November 14-16, 1967, fall joint computer conference
Domain-dependent single-agent search enhancements
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Chess-playing programs and the problem of complexity
IBM Journal of Research and Development
Review: Computer Shogi through 2000
CG '00 Revised Papers from the Second International Conference on Computers and Games
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In games where the number of legal moves is too high, it is not possible to do full-width search to a depth sufficient for good play. Plausible move generation (PMG) is an important search alternative in such domains. In this paper we propose a new method for plausible move generation in shogi. During move generation, Move Merit Analysis (MMA) gives a value to each move based on the plausible move generator(s) that generated the move. These values can be used for different cut-off schemes. We investigate the following alternatives: 1) Keep all moves with a positive MMA value; 2) Order the moves according to their MMA value and use cut-off thresholds to keep the best N moves. PMG with MMA and cut-off thresholds can save between 46% and 68% of the total number of legal moves with an accuracy between 99% and 93%. Tests show that all versions of shogi programs using PMG with MMA outperform an equivalent shogi program using full-width search. It is also shown that MMA is vital for our approach. Plausible move generation with MMA performs much better than plausible move generation without MMA. Cut-off thresholds improve the performance for N = 20 or N = 30.