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
Computers, Chess, and Cognition
Computers, Chess, and Cognition
Evolving Chess Playing Programs
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
The principal continuation and the killer heuristic
ACM '77 Proceedings of the 1977 annual conference
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
Plagiarism detection in game-playing software
Proceedings of the 4th International Conference on Foundations of Digital Games
Simulating human grandmasters: evolution and coevolution of evaluation functions
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Expert-driven genetic algorithms for simulating evaluation functions
Genetic Programming and Evolvable Machines
Optimizing the performance of GNU-chess with a genetic algorithm
Proceedings of the 13th International Conference on Humans and Computers
Three elemental game progress patterns
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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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 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 with a smaller number of parameters in its evaluation function to mimic the behavior of a superior mentor which uses a more extensive evaluation function. In principle, our mentor-assisted approach could be used in a wide range of problems for which appropriate mentors are available.