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Goal Directed Model Inversion: Learning Within Domain ConstraintsGoal Directed Model Inversion (GDMI) is an algorithm designed to generalize supervised learning to the case where target outputs are not available to the learning system. The output of the learning system becomes the input to some external device or transformation, and only the output of this device or transformation can be compared to a desired target. The fundamental driving mechanism of GDMI is to learn from success. Given that a wrong outcome is achieved, one notes that the action that produced that outcome "would have been right if the outcome had been the desired one." The algorithm makes use of these intermediate "successes" to achieve the final goal. A unique and potentially very important feature of this algorithm is the ability to modify the output of the learning module to force upon it a desired syntactic structure. This differs from ordinary supervised learning in the following way: in supervised learning the exact desired output pattern must be provided. In GDMI instead, it is possible to require simply that the output obey certain rules, i.e., that it "make sense" in some way determined by the knowledge domain. The exact pattern that will achieve the desired outcome is then found by the system. The ability to impose rules while allowing the system to search for its own answers in the context of neural networks is potentially a major breakthrough in two ways: (1) it may allow the construction of networks that can incorporate immediately some important knowledge, i.e., would not need to learn everything from scratch as normally required at present; and (2) learning and searching would be limited to the areas where it is necessary, thus facilitating and speeding up the process. These points are illustrated with examples from robotic path planning and parametric design.
Document ID
20020006942
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Colombano, Silvano P.
(NASA Ames Research Center Moffett Field, CA United States)
Compton, Michael
(RECOM Technologies, Inc. Moffett Field, CA United States)
Raghavan, Bharathi
(DeAnza Coll. CA United States)
Friedland, Peter
Date Acquired
August 20, 2013
Publication Date
January 1, 1994
Subject Category
Computer Programming And Software
Meeting Information
Meeting: International Conference on Artificial Neural Networks and Genetic Algorithms
Location: Ecole Des Mines d''Ales
Country: France
Start Date: April 18, 1995
End Date: April 21, 1995
Funding Number(s)
PROJECT: RTOP 233-03-04
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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