A computer program implements the algorithm described in "Hypothetical Scenario Generator for Fault-Tolerant Diagnosis" (NPO-42516), NASA Tech Briefs, Vol. 31, No. 6 (June 2007), page 71. To recapitulate: the Hypothetical Scenario Generator (HSG) is being developed in conjunction with other components of artificial-intelligence systems for automated diagnosis and prognosis of faults in spacecraft, aircraft, and other complex engineering systems. The HSG accepts, as input, possibly incomplete data on the current state of a system (see figure).

End-to-End Prognostic Architecture uses existing diagnostic models to generate predictions.
The HSG models a potential fault scenario as an ordered disjunctive tree of conjunctive consequences, wherein the ordering is based upon the likelihood that a particular conjunctive path will be taken for the given set of inputs. The computation of likelihood is based partly on a numerical ranking of the degree of completeness of data with respect to satisfaction of the antecedent conditions of prognostic rules. The results from the HSG are then used by a modelbased artificial-intelligence subsystem to predict realistic scenarios and states.

This program was written by Mark James and Ryan Mackey of Caltech for NASA's Jet Propulsion Laboratory.

This software is available for commercial licensing. Please contact Karina Edmonds of the California Institute of Technology at (626) 395-2322. Refer to NPO-43097.

This Brief includes a Technical Support Package (TSP).
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Generating Scenarios When Data Are Missing

(reference NPO-43097) is currently available for download from the TSP library.

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