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Evolving an expert checkers playing program without using human expertise

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2 Author(s)
K. Chellapilla ; Natural Selection Inc., La Jolla, CA, USA ; D. B. Fogel

An evolutionary algorithm has taught itself how to play the game of checkers without using features that would normally require human expertise. Using only the raw positions of pieces on the board and the piece differential, the evolutionary program optimized artificial neural networks to evaluate alternative positions in the game. Over the course of several hundred generations, the program taught itself to play at a level that is competitive with human experts (one level below human masters). This was verified by playing the best evolved neural network against 165 human players on an Internet gaming zone. The neural network's performance earned a rating that was better than 99.61% of all registered players at the Website. Control experiments between the best evolved neural network and a program that relies on material advantage indicate the superiority of the neural network both at equal levels of look ahead and CPU time. The results suggest that the principles of Darwinian evolution may he usefully applied to solving problems that have not yet been solved by human expertise

Published in:

IEEE Transactions on Evolutionary Computation  (Volume:5 ,  Issue: 4 )