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dc.contributor.authorPacella, Den
dc.contributor.authorPonticorvo, Men
dc.contributor.authorGigliotta, Oen
dc.contributor.authorMiglino, Oen
dc.date.accessioned2018-01-10T02:16:10Z
dc.date.available2018-01-10T02:16:10Z
dc.identifier.issn1613-0073en
dc.identifier.urihttp://hdl.handle.net/10026.1/10532
dc.description.abstract

The threat-sensitive predator avoidance hypothesis states that preys are able to assess the level of danger of the environment by using direct and in-direct predator cues. The existence of a neural system which determines this ability has been studied in many animal species like minnows, mosquitoes and wood frogs. What is still under debate is the role of evolution and learning for the emergence of this assessment system. We propose a bio-inspired computing model of how risk management can arise as a result of both factors and prove its impact on fitness in simulated robotic agents equipped with recurrent neural networks and evolved with genetic algorithm. The agents are trained and tested in environments with different level of danger and their performances are ana-lyzed and compared.

en
dc.format.extent93 - 105en
dc.language.isoenen
dc.titleA computational model of the evolution of antipredator behavior in situations with temporal variation of danger using simulated robotsen
dc.typeConference Contribution
plymouth.date-start2016-07-16en
plymouth.date-finish2016-07-17en
plymouth.date-finish2016-07-17en
plymouth.volume1895en
plymouth.conference-nameArtificial Intelligence and Cognition 2016en
plymouth.publication-statusPublisheden
plymouth.journalCEUR Workshop Proceedingsen
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/00 Groups by role
plymouth.organisational-group/Plymouth/00 Groups by role/Academics
plymouth.organisational-group/Plymouth/00 Groups by role/Post-Graduate Research Students
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Computing, Electronics and Mathematics
dc.publisher.placeNew York City, NY, USAen
dcterms.dateAccepted2016-07-16en
dc.rights.embargoperiodNot knownen
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.typeConference Paper/Proceeding/Abstracten


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