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dc.contributor.authorOlivier, Paul
dc.contributor.authorMoreno Aróstegui, Juan Manuel
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.identifier.citationOlivier, P.; Moreno, J. Equilibrium-Driven Adaptive Behavior Design. A: International Work-Conference on Artificial Neural Networks. "11th International Work-Conference on Artificial Neural Networks". Torremolinos: Springer Verlag, 2011, p. 589-596.
dc.description.abstractIn autonomous robotics, scalability is a primary discriminator for evaluating a behavior design methodology. Such a proposed methodology must also allow efficient and effective conversion from desired to implemented behavior. From the concepts of equilibrium and homeostasis, it follows that behavior could be seen as driven rather than controlled. Homeostatic variables allow the development of need elements to completely implement drive and processing elements in a synthetic nervous system. Furthermore, an autonomous robot or system must act with a sense of meaning as opposed to being a human-command executor. Learning is fundamental in adding adaptability, and its efficient implementation will directly improve scalability. It is shown how using classical conditioning to learn obstacle avoidance can be implemented with need elements instead of an existing artificial neural network (ANN) solution.
dc.format.extent8 p.
dc.publisherSpringer Verlag
dc.subjectÀrees temàtiques de la UPC::Informàtica::Robòtica
dc.subject.lcshNeural networks (Computer science)
dc.titleEquilibrium-Driven Adaptive Behavior Design
dc.typeConference report
dc.subject.lemacXarxes neuronals (Informàtica)
dc.contributor.groupUniversitat Politècnica de Catalunya. AHA - Arquitectures Hardware Avançades
dc.description.peerreviewedPeer Reviewed
dc.rights.accessRestricted access - publisher's policy
dc.description.versionPostprint (published version)
local.citation.authorOlivier, P.; Moreno, J.
local.citation.contributorInternational Work-Conference on Artificial Neural Networks
local.citation.publicationName11th International Work-Conference on Artificial Neural Networks

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