Edward Manning

Cycling has been an obstacle to coevolution of machine-learning agents. Monotonic algorithms seek continual improvement with respect to a solution concept; seeking an agent or set of agents that approaches the true solution without cycling. Algorithms that guarantee monotonicity generally require unlimited storage. One such algorithm is the Nash Me...

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...

This paper presents an artificial neural network with shared weights, trained to play the game of Othello by self-play with temporal difference learning (TDL). The network performs as well as the champion of the CEC 2006 Othello Evaluation Function Competition. The TDL-trained network contains only 67 unique weights compared to 2113 for the champio...