DID YOU KNOW? DTIC has over 3.5 million final reports on DoD funded research, development, test, and evaluation activities available to our registered users. Click HERE
to register or log in.
Issues in Model Based Troubleshooting,
MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB
Pagination or Media Count:
To determine why something has stopped working, its helpful to know how it was supposed to work in the first place. This simple fact underlies recent work on a number of systems that do diagnosis from knowledge about the internal structure and behavior of components of the malfunctioning device. Recently much work has been done in this vein in many domains with an apparent diversity of techniques. But the variety of domains and the variety of computational mechanisms used tom implement these systems tend to obscure two important facts. First, existing programs have similar mechanisms for generating and testing fault hypotheses. Second, most of these systems have similar built-in assumptions about both the devices being diagnosed and their failure modes these assumptions in turn limit the generality of the programs. The purpose of this paper is to identify the problems and non-problems in model based troubleshooting. The non-problems are in generating and testing fault hypotheses about misbehaving components in simple static devices a small core of largely equivalent techniques covers the apparent profusion of existing approaches. The problems occur with devices that arent static, arent simple, and whose components fail in ways current programs dont hypothesize and hence cant diagnose.
APPROVED FOR PUBLIC RELEASE