Affect and Learning: a computational analysis

Leiden Repository

Affect and Learning: a computational analysis

Type: Doctoral Thesis
Title: Affect and Learning: a computational analysis
Author: Broekens, Douwe Joost
Publisher: Leiden Institute of Advanced Computer Science (LIACS), Faculty of Science, Leiden University
Issue Date: 2007-12-18
Keywords: Cognitive psychology
Adaptive behavior
Artificial intelligence
Affective computing
Reinforcement learning
Instrumental conditioning
Human-computer interaction
Human-robot interaction
Formalisms
Appraisal theory
Abstract: In this thesis we have studied the influence of emotion on learning. We have used computational modelling techniques to do so, more specifically, the reinforcement learning paradigm. Emotion is modelled as artificial affect, a measure that denotes the positiveness versus negativeness of a situation to an artificial agent in a reinforcement learning setting. We have done a range of different experiments to study the effect of affect on learning, including the effect on learning if affect is used to control the exploration behaviour of the agent and the effect on learning when affect is communicated by a human (though real-time analysis of that human’s facial expressions) to a simulated robot. We conclude that affect is a useful concept to consider in adaptive agents that learn based on reinforcement learning and that in some cases affect can indeed help the learning process. Further, affective modelling in this way can help understand the psychological processes that underlie influences of affect on cognition. Finally, we have developed a formal notation for a specific type of emotion theory, i.e., cognitive appraisal theory.
Description: Promotor: J.N. Kok, Co-promotores: F.J. Verbeek, W.A. Kosters
With Summary in Dutch
Faculty: Faculteit der Wiskunde en Natuurwetenschappen
Citation: Broekens, D.J., 2007, Doctoral Thesis, Leiden University
ISBN: 9789088910241
Handle: http://hdl.handle.net/1887/12537
 

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application/pdf Summary in English_Conclusion 113.3Kb View/Open
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