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Learning to play games using a PSO-based competitive learning approach

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2 Author(s)
L. Messerschmidt ; Dept. of Comput. Sci., Univ. of Pretoria, South Africa ; A. P. Engelbrecht

A new competitive approach is developed for learning agents to play two-agent games. This approach uses particle swarm optimizers (PSO) to train neural networks to predict the desirability of states in the leaf nodes of a game tree. The new approach is applied to the TicTacToe game, and compared with the performance of an evolutionary approach. A performance criterion is defined to quantify performance against that of players making random moves. The results show that the new PSO-based approach performs well as compared with the evolutionary approach.

Published in:

IEEE Transactions on Evolutionary Computation  (Volume:8 ,  Issue: 3 )