Evolution of Signalling in a Group of Robots Controlled by Dynamic Neural Networks
Ampatzis Christos, Tuci Elio, Trianni Vito, Dorigo Marco


Communication is a point of central importance in swarms of robots. This paper describes a set of simulations in which artificial evolution is used as a means to engineer robot neuro-controllers capable of guiding groups of robots in a categorisation task by producing appropriate actions. In spite of the absence of explicit selective pressures (coded into the fitness function) which favour signalling over non-signalling groups, communicative behaviour emerges. Post-evaluation analyses illustrate the adaptive function of the evolved signals and show that they are tightly linked to the behavioural repertoire of the agents.

Swarm-bots project started
on October 1,2001
The project terminated
on March 31, 2005.
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Fri, 27 Jun 2014 11:26:47 +0200
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