Towards a general formula for analogical learning leading to more autonomous systems
Daniel E. Cooke, Dan W. Patterson, Scott A. Starks · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1991
In Greiner'' formalisms for an analogical learning system were introduced. The method of analogical learning which resulted from these formalisms depended upon human intervention in the form of " analogical hints" which related objects for which analogical inferences could be drawn. In the work introduced here the formalisms have been altered in a way that permits the " surmising" of the " analogical hints. " In particular a general formula for analogical learning is introduced. With respect to some object preliminary work is introduced which will result in a method for deriving unknown elements in the general formula from the known elements. Unknown/known elements in the general formula will include knowledge concerning the components which comprise an object knowledge concerning how to perform certain tasks using an object and how the object relates (from an analogical viewpoint) to other objects (i. e. the analogical hints). 1 . 0 OVERVIEW Learning is a key core technology in the development of intelligent robotic systems. In the past most efforts toward the development of learning systems to support robotic applications have centered upon the navigation and task planning issues. These approaches have combined the use of spatial/geometric reasoning with learning algorithms for example simulated annealing. The work reported herein demonstrates the first steps toward the development of a generalized learning system and is based upon work reported to NASA2. Based on these results we