Discovering Complex Othello Strategies through Evolutionary Neural Networks
David E. Moriarty, Risto P Miikkulainen · Connection Science · 1995
An approach to develop new game playing strategies based on artificial evolution of neural networks is presented. Evolution was directed to discover strategies in Othello against a random-moving opponent and later against an ff-fi search program. The networks discovered first a standard positional strategy, and subsequently a mobility strategy, an advanced strategy rarely seen outside of tournaments. The latter discovery demonstrates how evolutionary neural networks 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. Games 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 techniques. Most research in game playing has centered on creating deeper searches through the possible game scenarios. Deeper ...