Practical Issues in Temporal Difference Learning
Machine Learning
One jump ahead: challenging human supremacy in checkers
One jump ahead: challenging human supremacy in checkers
Co-Evolution in the Successful Learning of Backgammon Strategy
Machine Learning
Evolutionary Computation: The Fossil Record
Evolutionary Computation: The Fossil Record
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Computers, Chess, and Cognition
Computers, Chess, and Cognition
Evolving neural networks to play checkers without relying on expert knowledge
IEEE Transactions on Neural Networks
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Evolutionary algorithms can be used to learn how to play complex games of strategy without relying on human expertise. Here I discuss the use of evolutionary computation and artificial neural networks in learning how to play checkers. Starting from neural networks that were created randomly, an evolutionary algorithm has been able to craft a network that can play checkers at a nearly expert level. No features beyond the positions of pieces on the board and the piece differential were provided. The evolutionary algorithm learned everything else on its own, simply by playing the game.