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Reinforcement Learning in Autonomous Robots: An Empirical Investigation of the Role of Emotions (1999)
| Citations: | 20 - 3 self |
BibTeX
@MISC{Gadanho99reinforcementlearning,
author = {Sandra Pinto Clara do Carmo Gadanho},
title = {Reinforcement Learning in Autonomous Robots: An Empirical Investigation of the Role of Emotions },
year = {1999}
}
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Abstract
This thesis presents a study of the provision of emotions for artificial agents with the ultimate aim of enhancing their autonomy, i.e. making them more flexible, robust and self-sufficient. In recent years, the importance of emotions and their assistance to cognition has been increasingly acknowledged. Emotions are no longer considered undesirable or simply useless. Their role in various aspects of human and animal cognition like perception, attention, memory, decision-making and social interaction has been recognised as essential. The importance of emotions is much more evident insocial interaction and therefore much of the emotions research done in artificial systems focuses on the expression and recognition of emotions. However, recent neurophysiological research suggests that emotions also play a crucial part in cognition itself. This thesis investigates ways in which artificial emotions can improve autonomous behaviour in the domain of a simple, but complete, solitary learning agent. For this purpose, a non-symbolic emotion model was designed and implemented. It takes the form of a recurrent artificial neural network where emotions influence the perception
Keyphrases
empirical investigation autonomous robot reinforcement learning animal cognition emotion research artificial emotion recent year non-symbolic emotion model artificial system artificial agent various aspect ultimate aim crucial part recurrent artificial neural network evident insocial interaction social interaction autonomous behaviour recent neurophysiological research solitary learning agent
