Checkers Strategy Evolution with Speciated Neural Networks

Abstract

Checkers is a very simple game and easy to learn. Unlike chess, it is simple to move and needs a few rules. With respect to checkers, the evolutionary algorithm can discover a neural network that can be used to play at a near-expert level without injecting expert knowledge about how to play the game. Evolutionary approach does not need any prior knowledge to develop machine player but can develop high-level player. However, conventional evolutionary algorithms have a property of genetic drift that only one solution often dominates at the last generation. Because of genetic drift, it is difficult to discover diverse checkers players that have different properties in search space with simple evolutionary algorithm. Combining diverse solutions can make better performance than single dominating solution by complementing each other.

This research was supported by Brain Science and Engineering Research Program sponsored by Korean Ministry of Science and Technology.