Show simple item record Ampatzis, Christos Tuci, Elio Trianni, Vito Dorigo, Marco 2010-09-07T16:15:38Z 2010-09-07T16:15:38Z 2008-02-01
dc.identifier.citation Ampatzis , C , Tuci , E , Trianni , V & Dorigo , M 2008 , ' Evolution of Signaling in a Multi-Robot System: : Categorization and Communication ' Adaptive Behavior , vol 16 , no. 1 , pp. 5-26 . DOI: 10.1177/1059712307087282 en
dc.identifier.issn 1059-7123
dc.identifier.other PURE: 150510
dc.identifier.other PURE UUID: 29dfeae3-5093-4a9c-bc41-47ee007a0497
dc.identifier.other dspace: 2160/5399
dc.identifier.other DSpace_20121128.csv: row: 3635
dc.identifier.other RAD: 9519
dc.identifier.other RAD_Outputs_All_ID_Import_20121105.csv: row: 2957
dc.identifier.other RAD: 9543
dc.identifier.other RAD_Outputs_All_ID_Import_20121105.csv: row: 2979
dc.identifier.other Scopus: 38349081650
dc.description Ampatzis, C., Tuci, E., Trianni, V., Dorigo, M. (2008). Evolution of Signaling in a Multi-Robot System: Categorization and Communication. Adaptive Behavior, 16 (1), 5-26 en
dc.description.abstract Communication is of central importance in collective robotics, as it is integral to the switch from solitary to social behavior. In this article, we study emergent communication behaviors that are not predetermined by the experimenter, but are shaped by artificial evolution, together with the rest of the behavioral repertoire of the robots. In particular, we describe a set of experiments in which artificial evolution is used as a means to engineer robot neuro-controllers capable of guiding groups of robots in a categorization task by producing appropriate actions. The categorization is a result of how robots' sensory inputs unfold in time, and, more specifically, of the integration over time of sensory input. In spite of the absence of explicit selective pressure (coded into the fitness function), which would favor signaling over non-signaling groups, communicative behavior emerges. Post-evaluation analyses illustrate the adaptive function of the evolved signals and show that these signals are tightly linked to the behavioral repertoire of the agents. Signals evolve because communication enhances group performance, revealing a “hidden” benefit for social behavior. This benefit is related to obtaining robust and fast decision-making mechanisms. More generally, we show how processes requiring the categorization of noisy dynamical information might be improved by social interactions mediated by communication. In a further series of experiments, we successfully download evolved controllers onto real s-bots. We discuss the challenges involved in porting neuro-controllers displaying time-based decision-making processes onto real robots. Finally, the beneficial effect of communication is shown to transfer to the case of a real robot, and the robustness of the behavior against inter-robot differences is discussed. en
dc.format.extent 22 en
dc.language.iso eng
dc.relation.ispartof Adaptive Behavior en
dc.rights en
dc.subject communication en
dc.subject decision-making en
dc.subject real robots en
dc.subject signaling en
dc.subject swarm robotics en
dc.title Evolution of Signaling in a Multi-Robot System: : Categorization and Communication en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Intelligent Robotics Group en
dc.description.status Peer reviewed en

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