Cooperative area extension of PSO: Transfer learning vs. uncertainty in a simulated swarm robotics - 文章详情页

作者：

Atyabi, Adham (Atyabi, Adham.) | Powers, David M.W. (Powers, David M.W..)

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EI Scopus

摘要：

The　study　investigates　the　effectiveness　of　2　variations　of　Particle　Swarm　Optimization　(PSO)　called　Area　Extended　PSO　(AEPSO)　and　Cooperative　AEPSO　(CAEPSO)　in　simulated　robotic　environments　affected　by　a　combinatorial　noise.　Knowledge　Transfer,　the　use　of　the　expertise　and　knowledge　gained　from　previous　experiments,　can　improve　the　robots　decision　making　and　reduce　the　number　of　wrong　decisions　in　such　uncertain　environments.　This　study　investigates　the　impact　of　transfer　learning　on　robots＇　performance　in　such　hostile　environment.　The　results　highlight　the　feasibility　of　CAEPSO　to　be　used　as　the　controller　and　decision　maker　of　a　swarm　of　robots　in　the　simulated　uncertain　environment　when　gained　expertise　from　past　training　is　transferred　to　the　robots　in　the　testing　phase.

关键词：

Knowledge management Particle swarm optimization (PSO) Robotics Robots Swarm intelligence Decision making

作者机构：

[ 1 ] [Atyabi, Adham]CSEM Centre for Knowledge and Interaction Technology, Flinders University, Adelaide, SA, Australia
[ 2 ] [Powers, David M.W.]CSEM Centre for Knowledge and Interaction Technology, Flinders University, Adelaide, SA, Australia
[ 3 ] [Powers, David M.W.]Beijing Municipal Lab. for Multimedia and Intelligent Software, Beijing University of Technology, Beijing, China

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