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dc.contributor.authorKirtay, M.
dc.contributor.authorVannucci, L.
dc.contributor.authorFalotico, E.
dc.contributor.authorÖztop, Erhan
dc.contributor.authorLaschi, C.
dc.date.accessioned2017-07-26T07:21:30Z
dc.date.available2017-07-26T07:21:30Z
dc.date.issued2017
dc.identifier.issn2164-0580en_US
dc.identifier.urihttp://hdl.handle.net/10679/5479
dc.identifier.urihttp://ieeexplore.ieee.org/document/7803408/
dc.description.abstractCertain emotions and moods can be manifestations of complex and costly neural computations that our brain wants to avoid. Instead of reaching an optimal decision based on the facts, we find it often easier and sometimes more useful to rely on hunches. In this work, we extend a previously developed model for such a mechanism where a simple neural associative memory was used to implement a visual recall system for a humanoid robot. In the model, the changes in the neural state consume (neural) energy, and to minimize the total cost and the time to recall a memory pattern, the robot should take the action that will lead to minimal neural state change. To do so, the robot needs to learn to act rationally, and for this, it has to explore and find out the cost of its actions in the long run. In this study, a humanoid robot (iCub) is used to act in this scenario. The robot is given the sole action of changing his gaze direction. By reinforcement learning (RL) the robot learns which state-action pair sequences lead to minimal energy consumption. More importantly, the reward signal for RL is not given by the environment but obtained internally, as the actual neural cost of processing an incoming visual stimuli. The results indicate that reinforcement learning with the internally generated reward signal leads to non-trivial behaviours of the robot which might be interpreted by external observers as the robot's `liking' of a specific visual pattern, which in fact emerged solely based on the neural cost minimization principle.en_US
dc.description.sponsorshipEuropean Commission ; Italian Ministry of Foreign Affairs, General Directorate for the Promotion of the "Country System", Bilateral and Multilateral Scientific and Technological Cooperation Unit
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/604102
dc.relation.ispartofHumanoid Robots (Humanoids), 2016 IEEE-RAS 16th International Conference onen_US
dc.rightsrestrictedAccess
dc.titleSequential decision making based on emergent emotion for a humanoid roboten_US
dc.typeConference paperen_US
dc.peerreviewedyes
dc.publicationstatuspublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0002-3051-6038 & YÖK ID 45227) Öztop, Erhan
dc.contributor.ozuauthorÖztop, Erhan
dc.identifier.wosWOS:000403009300163
dc.identifier.doi10.1109/HUMANOIDS.2016.7803408en_US
dc.subject.keywordsRobotsen_US
dc.subject.keywordsVisualizationen_US
dc.subject.keywordsDecision makingen_US
dc.subject.keywordsHeuristic algorithmsen_US
dc.subject.keywordsEnergy consumptionen_US
dc.subject.keywordsBiologyen_US
dc.subject.keywordsCamerasen_US
dc.identifier.scopusSCOPUS:2-s2.0-85010223921
dc.contributor.authorMale1


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