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.
CitacióAgostini, A.G. [et al.]. Action rule induction from cause-effect pairs learned through robot-teacher interaction. A: International Conference on Cognitive Systems. "Proceedings of 2008 International Conference on Cognitive Systems". Karlsruhe: 2008, p. 213-218.