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.
CitationAgostini, Alejandro; Celaya, Enric; Torras, Carme; Wörgötter, Florentin. "Action rule induction from cause-effect pairs learned through robot-teacher interaction". A: 2008 International Conference on Cognitive Systems (CogSys), Karlsruhe, Alemanya, 2008. University of Karlsruhe, 2008, p. 213-218.
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