Evolving Complex Othello Strategies Using Marker-Based Genetic Encoding ofNeural Networks
David E. Moriarty, Risto P Miikkulainen · 1993
A system based on artificial evolution of neural networks for developing new game playing strategies is presented. The system uses marker-based genes to encode nodes in a neural network. The game-playing networks were forced to evolve sophisticated strategies in Othello to compete first with a random mover and then with an ff-fi search program. Without any direction, the networks discovered first the standard positional strategy, and subsequently the mobility strategy, an advanced strategy rarely seen outside of tournaments. The latter discovery demonstrates how evolution can develop novel solutions by turning an initial disadvantage into an advantage in a changed environment. 1 Introduction Game playing is one of the oldest and most extensively studied areas of artificial intelligence. Game playing appears to require sophisticated intelligence in a well-defined problem where success is easily measured. Games have therefore proven to be important domains for studying problem solving ...