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Towards Emotional Modulation of Action Selection in Motivated Autonomous Robots
Abstract and Figures
“Behavior-based” artificial intelligence (AI) proposes a “bottom-up” or “synthetic” approach to study intelligence in the context of “complete” autonomous agents. This paradigm is highly inspired by animal behavior, neuroscience and evolution theory, and it is appropriate to deal with situations that require a system to autonomously carry out several time-dependent tasks in a dynamic, unpredictable environment. One of the main problems arising in those situations is action selection, i.e., making a decision as to what behavior to execute next in order to carry out several conflicting tasks and survive in the environment. Although many action selection architectures have been proposed, the literature lacks systematic ways to analyze their properties and their performance. In this thesis I firstly propose both quantitative and qualitative analyses of action selection architectures. I compare different motivation-based architectures within virtual and robotic frameworks and show the adequacy of “winner-take-all” and “voting-based” architectures for different environmental conditions. I then show that these architectures can greatly improve their adaptivity if modulated by second-order controllers. Inspired by neuroscience, I propose underlying mechanisms of emotions as second-order controllers or modulators of motivation-based action selection architectures. Acting only at the sensory level of the architectures and without modifying their structure, these mechanisms generate behavior adapted to changing environmental conditions. As a result of such modulation different functionalities emerge. This research departs from more “top-down” engineering approaches to emotion modeling, which traditionally implement those functionalities as predefined “emotion modules”.
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