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Autonomous agent response learning by a multi-species particle swarm optimization

Chi-kin Chow, Hung-tat Tsui
Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)  

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Abrlrocr -A novel autonomous agent response learning (AARL) algorithm is presented in this paper. We proposed to decompose the award function into a set of local award functions. By optimizing this objective function set, the response function with maximum award c m be determined. To tackle the optimization problem, a modified Particle Swarm Optimization (PSO) called "MultiSpecies PSO (MS-PSO)" is introduced by considering each objective function as a specie swarm. TWO sets of experiments are
more » ... ovided to illustrate the perfurmance.af MS-PSO. The results show that it returns B more accurate response set within shorter duration by comparing with other PSO methods.
doi:10.1109/cec.2004.1330938 dblp:conf/cec/ChowT04 fatcat:i2x7teem4nb2de5puxi4tahjde
Citation

Chi-kin Chow, Hung-tat Tsui. "Autonomous agent response learning by a multi-species particle swarm optimization." Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753) 778-785