Using Resource-Limited Nash Memory to Improve an Othello Evaluation Function

Finding the best strategy for winning a game using self-play or coevolution can be hindered by intransitivity among strategies and a changing fitness landscape. Nash Memory has been proposed as an archive for coevolution, to counter intransitivity and provide a more consistent fitness landscape. A lack of bounds on archive size might impede its use in a large, complex domain, such as the game of Othello, with strategies described by n -tuple networks. This paper demonstrates that even with a bounded-size archive, an evolving population can continue to show progress past the point where self-play no longer can. Characteristics of Nash equilibria are shown to be valuable in the measurement of performance. In addition, a technique for automated selection of features is demonstrated for the n-tuple networks.