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Equilibrium-Driven Adaptive Behavior Design
dc.contributor.author | Olivier, Paul |
dc.contributor.author | Moreno Aróstegui, Juan Manuel |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica |
dc.date.accessioned | 2011-11-18T19:30:20Z |
dc.date.available | 2011-11-18T19:30:20Z |
dc.date.created | 2011 |
dc.date.issued | 2011 |
dc.identifier.citation | Olivier, 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.identifier.isbn | 978-3-642-21497-4 |
dc.identifier.uri | http://hdl.handle.net/2117/13975 |
dc.description.abstract | In 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.extent | 8 p. |
dc.language.iso | eng |
dc.publisher | Springer Verlag |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Robòtica |
dc.subject.lcsh | Neural networks (Computer science) |
dc.subject.lcsh | Robotics |
dc.title | Equilibrium-Driven Adaptive Behavior Design |
dc.type | Conference report |
dc.subject.lemac | Robòtica |
dc.subject.lemac | Xarxes neuronals (Informàtica) |
dc.contributor.group | Universitat Politècnica de Catalunya. AHA - Arquitectures Hardware Avançades |
dc.identifier.doi | 10.1007/978-3-642-21498-1_74 |
dc.description.peerreviewed | Peer Reviewed |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 8647252 |
dc.description.version | Postprint (published version) |
local.citation.author | Olivier, P.; Moreno, J. |
local.citation.contributor | International Work-Conference on Artificial Neural Networks |
local.citation.pubplace | Torremolinos |
local.citation.publicationName | 11th International Work-Conference on Artificial Neural Networks |
local.citation.startingPage | 589 |
local.citation.endingPage | 596 |