Paper Info
Title
Mood as an affective component for robotic behavior with continuous adaptation via Learning Momentum
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.
Year
DOI
Venue
2010
10.1109/ICHR.2010.5686845
Humanoids
Keywords
Field
DocType
robotic behavior,navigation performance,time-varying systems,tame mood component,learning (artificial intelligence),learning momentum,behavioural sciences,continuous adaptation,intelligent robots,path planning,time varying affective model,learning artificial intelligence,real time,robot kinematics,graphical user interfaces,real time systems
Motion planning,Big Five personality traits,Mood,Simulation,Computer science,Robot kinematics,Parametric statistics,Graphical user interface,Behavioural sciences,Affect (psychology)
Conference
ISBN
Citations 
PageRank 
978-1-4244-8689-2
1
0.35
References 
Authors
10
3
Name
Order
Citations
PageRank
Sunghyun Park1142.65
Lilia Moshkina2707.13
Ronald C. Arkin32921564.82