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

Abstract

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… (More)
DOI: 10.1109/TEVC.2004.826070

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@article{Messerschmidt2004LearningTP, title={Learning to play games using a PSO-based competitive learning approach}, author={L. Messerschmidt and Andries Petrus Engelbrecht}, journal={IEEE Transactions on Evolutionary Computation}, year={2004}, volume={8}, pages={280-288} }