A draughts learning system based on neural networks and temporal differences: The impact of an efficient tree-search algorithm

Abstract

The NeuroDraughts is a good automatic draughts player which uses temporal difference learning to adjust the weights of an artificial neural network whose role is to estimate how much the board state represented in its input layer by NET-FEATUREMAP is favorable to the player agent. The set of features is manually defined. The search for the best action corresponding to a current state board is performed by minimax algorithm. This paper presents new and very successful results obtained by substituting an efficient tree-search module based on alpha-beta pruning, transposition table and iterative deepening for the minimax algorithm in NeuroDraughts. The runtime required for training the new player was drastically reduced and its performance was significantly improved. © 2008 Springer Berlin Heidelberg.