The History Heuristic and Alpha-Beta Search Enhancements in Practice
IEEE Transactions on Pattern Analysis and Machine Intelligence
Practical Issues in Temporal Difference Learning
Machine Learning
Learning to Play Chess Using Temporal Differences
Machine Learning
Multi-cut &agr;&bgr;-pruning in game-tree search
Theoretical Computer Science
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolving Chess Playing Programs
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Multi-cut Pruning in Alpha-Beta Search
CG '98 Proceedings of the First International Conference on Computers and Games
The principal continuation and the killer heuristic
ACM '77 Proceedings of the 1977 annual conference
Genetic algorithms for mentor-assisted evaluation function optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
CG '08 Proceedings of the 6th international conference on Computers and Games
Temporal difference learning applied to a high-performance game-playing program
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Evolution of an efficient search algorithm for the mate-in-N problem in chess
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
GP-EndChess: using genetic programming to evolve chess endgame players
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Blockage detection in pawn endings
CG'04 Proceedings of the 4th international conference on Computers and Games
An evolutionary algorithm with a history mechanism for tuning a chess evaluation function
Applied Soft Computing
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In this paper we demonstrate how genetic algorithms can be used to reverse engineer an evaluation function's parameters for computer chess. Our results show that using an appropriate expert (or mentor), we can evolve a program that is on par with top tournament-playing chess programs, outperforming a two-time World Computer Chess Champion. This performance gain is achieved by evolving a program that mimics the behavior of a superior expert. The resulting evaluation function of the evolved program consists of a much smaller number of parameters than the expert's. The extended experimental results provided in this paper include a report on our successful participation in the 2008 World Computer Chess Championship. In principle, our expert-driven approach could be used in a wide range of problems for which appropriate experts are available.