Integrating Knowledge-Based System and Neural Network Techniques for Robotic Skill Acquisition

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. I N T R O D U C T I O N Neural networks have been shown to be very efficient at learning from experience. However, there area number of problems to be overcome before they become useful components of truly autonomous learning systems. Presently, training data must be supplied by an outside operator who must closely supervise the learning process. Also, if the system containing the neural network is required to perform its...

• Type: Conference
• Year: 1989
• Where: IJCAI
• Authors: David Handelman, Stephen Lane, Jack Gelfand
• Keywords: Autonomous Learning Systems, IJCAI 1989, IJCAI 2007, Learning Process, Neural Network