Publication
Action rule induction from cause-effect pairs learned through robot-teacher interaction
Conference Article
Conference
International Conference on Cognitive Systems (228)
Edition
2008
Pages
213-218
Doc link
File
Authors
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Agostini, Alejandro Gabriel
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Celaya Llover, Enric
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Torras Genís, Carme
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Wörgötter, Florentin
Projects associated
Abstract
In this work we propose a decision-making system that efficiently learns behaviors in the form of rules using natural human instructions about cause-effect relations in currently observed situations, avoiding complicated instructions and explanations of long-run action sequences and complete world dynamics. The learned rules are represented in a way suitable to both reactive and deliberative approaches, which are thus smoothly integrated. Simple and repetitive tasks are resolved reactively, while complex tasks would be faced in a more deliberative manner using a planner module. Human interaction is only required if the system fails to obtain the expected results when applying a rule, or fails to resolve the task with the knowledge acquired so far.
Categories
intelligent robots, learning (artificial intelligence), planning (artificial intelligence).
Scientific reference
A. Agostini, E. Celaya, C. Torras and F. Wörgötter. Action rule induction from cause-effect pairs learned through robot-teacher interaction, 2008 International Conference on Cognitive Systems, 2008, Karlsruhe, Alemanya, pp. 213-218, University of Karlsruhe.
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