Evolving neural networks to play checkers without relying on expert knowledge

An experiment was conducted where neural networks compete for survival in a n evolving population based on their ability to play checkers. More specifi cally, multilayer feedforward neural networks were used to evaluate alterna tive board positions and games were played using a minimax search strategy. At each generation, the extant neural networks were paired in competitions and selection was used to eliminate those that performed poorly relative t o other networks. Offspring neural networks were created from the survivors using random variation of all weights and bias terms. After a series of 25 0 generations, the best-evolved neural network was played against human opp onents in a series of 90 games on an internet website, The neural network w as able to defeat two expert-level players and played to a draw against a m aster. The final rating of the neural network placed it in the "Class A" ca tegory using a standard rating system. Of particular importance in the desi gn of the experiment was the fact that no features beyond the piece differe ntial were given to the neural networks as a priori knowledge. The process of evolution was able to extract all of the additional information required to play at this level of competency. It accomplished this based almost sol ely on the feedback offered in the final aggregated outcome of each game pl ayed (i.e., win, lose, or draw). This procedure stands in marked contrast t o the typical artifice of explicitly injecting expert knowledge into a game -playing program.