Integrating knowledge-based system and neural network techniques for robotic skill acquisition

Authors:
David A. Handelman;Stephen H. Lane;Jack J. Gelfand
Affiliations:
Human Information Processing Group, Department of Psychology, Princeton University, Princeton, New Jersey;Human Information Processing Group, Department of Psychology, Princeton University, Princeton, New Jersey;Human Information Processing Group, Department of Psychology, Princeton University, Princeton, New Jersey
Venue:
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Year:
1989

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Abstract

This paper describes an approach to robotic control that is patterned after models of human skill acquisition. The intent is to develop robots capable of learning how to accomplish complex tasks using designer-supplied instructions and self induced practice. A simulation is presented in which a rule-based system supervises the training of a neural network and controls the operation of the system during the learning process. Simulation results show the interaction between rule-based and network-based system components during various phases of training and supervision.