Experience-based learning experiments using Go-Moku
William T. Katz, Steven Pham · 2002
Three experience-based learning techniques are explored using the game Go-Moku (Connect-5). The first method, an exception tree, is used to prevent poor lines of play by recording critical moves in a move tree. The second method simply records all games in a large experience tree, backtracking the eventual outcomes in traditional minimax fashion. Both techniques allow a computer player to modify its behavior based on past experience, and, therefore, typically defeat static game programs. The last method explored is the use of a multilayer feedforward artificial neural network for strategy calculation. The network was trained on selected interior nodes of the experience tree using the backpropagation algorithm. The neural network strategy algorithm compares favorably with a fine-tuned hand-crafted strategy algorithm.>