Title:
Mood as an Affective Component for Robotic Behavior with Continuous Adaptation via Learning Momentum
Mood as an Affective Component for Robotic Behavior with Continuous Adaptation via Learning Momentum
Author(s)
Park, Sunghyun
Moshkina, Lilia
Arkin, Ronald C.
Moshkina, Lilia
Arkin, Ronald C.
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Abstract
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
systems
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Date Issued
2010
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Text
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Paper