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
Genetic algorithms for mentor-assisted evaluation function optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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
An evolutionary algorithm with a history mechanism for tuning a chess evaluation function
Applied Soft Computing
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This paper demonstrates the use of genetic algorithms for evolving a grandmaster-level evaluation function for a chess program. This is achieved by combining supervised and unsupervised learning. In the supervised learning phase the organisms are evolved to mimic the behavior of human grandmasters, and in the unsupervised learning phase these evolved organisms are further improved upon by means of coevolution. While past attempts succeeded in creating a grandmaster-level program by mimicking the behavior of existing computer chess programs, this paper presents the first successful attempt at evolving a state-of-the-art evaluation function by learning only from databases of games played by humans. Our results demonstrate that the evolved program outperforms a two-time World Computer Chess Champion.