A computer program implements an adaptive problem-solving (APS) algorithm for real-time development, refinement, and maintenance of the control strategy of an autonomous system that must operate in an environment about which little or no detailed information is available in advance. In the initial application for which the APS software was developed, the autonomous system would be an exploratory spacecraft that would feature a flexible control architecture and would be equipped with planning and scheduling software, which the APS software would complement. Given a generic set of control strategies, the APS software evaluates the strategies, performs "what-if" analyses, and utilizes statistical methods to rank each strategy or generate a more appropriate strategy in face of current information about the environment. In an iterative process of reinforcement learning, the highest-ranked strategies are passed back and forth between the APS algorithm and a search algorithm until a stopping criterion is satisfied. The user can specify the allowable error in the choice of the best control strategy. In this choice, other things being equal, strategies that can be evaluated accurately by use of fewer data points are favored over those for which more data points are needed.

This program was written by Steve Chien, Barbara Engelhardt, Darren Mutz, and Robert Sherwood of Caltech for NASA's Jet Propulsion Laboratory. For further information, access the Technical Support Package (TSP) free on-line at www.nasatech.com/tsp  under the Software category.

This software is available for commercial licensing. Please contact Don Hart of the California Institute of Technology at (818) 393-3425. Refer to

NPO-21071.


This Brief includes a Technical Support Package (TSP).
Adaptive Problem Solving for Refining Control Strategy

(reference NPO-21071) is currently available for download from the TSP library.

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This article first appeared in the October, 2001 issue of NASA Tech Briefs Magazine.

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