Mood as an Affective Component for Robotic Behavior with Continuous Adaptation via Learning Momentum


The design and implementation of mood as an affective component for robotic behavior is described in the context of the TAME framework – a comprehensive, time-varying affective model for robotic behavior that encompasses personality traits, attitudes, moods, and emotions. Furthermore, a method for continuously adapting TAME’s Mood component (and thereby the overall affective system) to individual preference is explored by applying Learning Momentum, which is a parametric adjustment learning algorithm that has been successfully applied in the past to improve navigation performance in real-time, reactive robotic system

Similar works

Full text


Scholarly Materials And Research @ Georgia Tech

Provided a free PDF
oaioai:smartech.gatech.edu:1853/43202Last time updated on 6/21/2012View original full text link

Having an issue?

Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.