Engineering topics

Stclair, D. C.

Publications and source records attributed to Stclair, D. C..

3 records

NASA NTRS1988

Functional reasoning in diagnostic problem solving

This work is one facet of an integrated approach to diagnostic problem solving for aircraft and space systems currently under development. The authors are applying a method of modeling and reasoning about deep knowledge based on a functional viewpoint. The approach recognizes a level of device understanding which is intermediate between a compiled level of typical Expert Systems, and a deep level at which large-scale device behavior is derived from known properties of device structure and component behavior. At this intermediate functional level, a device is modeled in three steps. First, a component decomposition of the device is defined. Second, the functionality of each device/subdevice is abstractly identified. Third, the state sequences which implement each function are specified. Given a functional representation and a set of initial conditions, the functional reasoner acts as a consequence finder. The output of the consequence finder can be utilized in diagnostic problem solving. The paper also discussed ways in which this functional approach may find application in the aerospace field.

Sticklen, Jon↗

NASA NTRS1988

Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base.

Stclair, D. C.↗

NASA NTRS1986

Integration of an expert system into a user interface language demonstration

The need for a User Interface Language (UIL) has been recognized by the Space Station Program Office as a necessary tool to aid in minimizing the cost of software generation by multiple users. Previous history in the Space Shuttle Program has shown that many different areas of software generation, such as operations, integration, testing, etc., have each used a different user command language although the types of operations being performed were similar in many respects. Since the Space Station represents a much more complex software task, a common user command language--a user interface language--is required to support the large spectrum of space station software developers and users. To assist in the selection of an appropriate set of definitions for a UIL, a series of demonstration programs was generated with which to test UIL concepts against specific Space Station scenarios using operators for the astronaut and scientific community. Because of the importance of expert system in the space station, it was decided that an expert system should be embedded in the UIL. This would not only provide insight into the UIL components required but would indicate the effectiveness with which an expert system could function in such an environment.

Stclair, D. C.↗