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Reducing a Knowledge-Base Search Space When Data Are MissingThis software addresses the problem of how to efficiently execute a knowledge base in the presence of missing data. Computationally, this is an exponentially expensive operation that without heuristics generates a search space of 1 + 2n possible scenarios, where n is the number of rules in the knowledge base. Even for a knowledge base of the most modest size, say 16 rules, it would produce 65,537 possible scenarios. The purpose of this software is to reduce the complexity of this operation to a more manageable size. The problem that this system solves is to develop an automated approach that can reason in the presence of missing data. This is a meta-reasoning capability that repeatedly calls a diagnostic engine/model to provide prognoses and prognosis tracking. In the big picture, the scenario generator takes as its input the current state of a system, including probabilistic information from Data Forecasting. Using model-based reasoning techniques, it returns an ordered list of fault scenarios that could be generated from the current state, i.e., the plausible future failure modes of the system as it presently stands. The scenario generator models a Potential Fault Scenario (PFS) as a black box, the input of which is a set of states tagged with priorities and the output of which is one or more potential fault scenarios tagged by a confidence factor. The results from the system are used by a model-based diagnostician to predict the future health of the monitored system.
Document ID
20090041683
Acquisition Source
Jet Propulsion Laboratory
Document Type
Other - NASA Tech Brief
Authors
James, Mark
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
August 24, 2013
Publication Date
March 1, 2007
Publication Information
Publication: NASA Tech Briefs, March 2007
Subject Category
Man/System Technology And Life Support
Report/Patent Number
NPO-42818
Distribution Limits
Public
Copyright
Public Use Permitted.
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