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Proceedings Paper

Towards a general formula for analogical learning leading to more autonomous systems
Author(s): Daniel E. Cooke; Dan W. Patterson; Scott A. Starks
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Paper Abstract

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

Paper Details

Date Published: 1 February 1991
PDF: 7 pages
Proc. SPIE 1381, Intelligent Robots and Computer Vision IX: Algorithms and Techniques, (1 February 1991); doi: 10.1117/12.25162
Show Author Affiliations
Daniel E. Cooke, Univ. of Texas/El Paso (United States)
Dan W. Patterson, Univ. of Texas/El Paso (United States)
Scott A. Starks, Univ. of Texas/El Paso (United States)

Published in SPIE Proceedings Vol. 1381:
Intelligent Robots and Computer Vision IX: Algorithms and Techniques
David P. Casasent, Editor(s)

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