Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Experiments in search and knowledge
Experiments in search and knowledge
Measuring the performance potential of chess programs
Artificial Intelligence - Special issue on computer chess
The development of a world class Othello program
Artificial Intelligence - Special issue on computer chess
How computers play chess
One jump ahead: challenging human supremacy in checkers
One jump ahead: challenging human supremacy in checkers
Chess Skill in Man and Machine
Chess Skill in Man and Machine
Performance analysis of the technology chess program.
Performance analysis of the technology chess program.
Scalable Search in Computer Chess: Algorithmic Enhancements and Experiments at High Search Depths (Computational Intelligence)
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This paper presents the results of a new self-play experiment in computer chess. It is the first such experiment ever to feature search depths beyond 9 plies and thousands of games for every single match. Overall, we executed 24,000 self-play games (3,000 per match) in one "calibration"match and seven "depth X+1⇔X" handicap matches at fixed iteration depths ranging from 5-12 plies. For the experiment to be realistic and independently repeatable, we relied on a state-of-the-art commercial contestant: Fritz 6, one of the strongest modern chess programs available. The main result of our new experiment is that it shows the existence of diminishing returns for additional search in computer chess selfplay by FRITZ 6 with 95% statistical confidence. The average rating gain per search iteration shrinks by half from 169 ELO at 6 plies to 84 ELO at 12 plies. The diminishing returns manifest themselves by declining rates of won games and reversely increasing rates of drawn games for the deeper searching program versions. Their rates of lost games, however, remain quite steady for the whole depth range of 5-12 plies.