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

An 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 can be determined. To tackle the optimization proble...

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Bibliographic Details
Published inProceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753) Vol. 1; pp. 778 - 785 Vol.1
Main Authors CHOW, Chi-Kin, TSUI, Hung-Tat
Format Conference Proceeding
LanguageEnglish
Published Piscataway NJ IEEE 2004
Subjects
Applied sciences
Artificial intelligence
Autonomous agents
Computer science; control theory; systems
Constraint optimization
Exact sciences and technology
Laboratories
Manufacturing automation
Neural networks
Optimization methods
Particle swarm optimization
Remotely operated vehicles
Robotics and automation
Signal processing algorithms
Autonomous system
Response function
Multiagent system
Evolutionary algorithm
Duration
Swarm intelligence
Objective function
Neural network
Learning algorithm
Artificial intelligence
Mathematical programming
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Summary:An 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 can be determined. To tackle the optimization problem, a modified particle swarm optimization (PSO) called "multi-species PSO (MS-PSO)" is introduced by considering each objective function as a specie swarm. Two sets of experiments are provided to illustrate the performance of MS-PSO. The results show that it returns a more accurate response set within shorter duration by comparing with other PSO methods.
ISBN:9780780385153
0780385152
DOI:10.1109/CEC.2004.1330938