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GECCO
2003
Springer

Multi-agent Learning of Heterogeneous Robots by Evolutionary Subsumption

10 years 3 months ago
Multi-agent Learning of Heterogeneous Robots by Evolutionary Subsumption
Abstract. Many multi-robot systems are heterogeneous cooperative systems, systems consisting of different species of robots cooperating with each other to achieve a common goal. This paper presents the emergence of cooperative behaviors of heterogeneous robots by means of GP. Since directly using GP to generate a controller for complex behaviors is inefficient and intractable, especially in the domain of multi-robot systems, we propose an approach called Evolutionary Subsumption, which applies GP to subsumption architecture. We test our approach in an “eye”-“hand” cooperation problem. By comparing our approach with direct GP and artificial neural network (ANN) approaches, our experimental results show that ours is more efficient in emergence of complex behaviors.
Hongwei Liu, Hitoshi Iba
Added 06 Jul 2010
Updated 06 Jul 2010
Type Conference
Year 2003
Where GECCO
Authors Hongwei Liu, Hitoshi Iba
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