Shrobe

Bio: Shrobe is an academic researcher. The author has contributed to research in topics: Troubleshooting & Expert system. The author has an hindex of 1, co-authored 1 publications receiving 55 citations.

Representing Structure and Behavior of Digital Hardware

Abstract: The development of expert systems for troubleshooting digital electronics is considered. It is shown that suitably represented descriptions of structure and behavior set an important foundation. The authors approach offers a unity of device description and simulation, since the descriptions themselves are runnable. Unsolved problems are noted. 10 references.

What Is a Knowledge Representation

Abstract: Although knowledge representation is one of the central and, in some ways, most familiar concepts in AI, the most fundamental question about it -- What is it? -- has rarely been answered directly. Numerous papers have lobbied for one or another variety of representation, other papers have argued for various properties a representation should have, and still others have focused on properties that are important to the notion of representation in general. In this article, we go back to basics to address the question directly. We believe that the answer can best be understood in terms of five important and distinctly different roles that a representation plays, each of which places different and, at times, conflicting demands on the properties a representation should have. We argue that keeping in mind all five of these roles provides a usefully broad perspective that sheds light on some longstanding disputes and can invigorate both research and practice in the field.

Diagnostic Reasoning Based on Structure and Behavior

Abstract: Abstract We describe a system that reasons from first principles, i.e., using knowledge of structure and behavior. The system has been implemented and tested on several examples in the domain of troubleshooting digital electronic circuits. We give an example of the system in operation, illustrating that this approach provides several advantages, including a significant degree of device independence, the ability to constrain the hypotheses it considers at the outset, yet deal with a progressively wider range of problems, and the ability to deal with situations that are novel in the sense that their outward manifestations may not have been encountered previously. As background we review our basic approach to describing structure and behavior, then explore some of the technologies used previously in troubleshooting. Difficulties encountered there lead us to a number of new contributions, four of which make up the central focus of this paper. • — We describe a technique we call constraint suspension that provides a powerful tool for troubleshooting. • — We point out the importance of making explicit the assumptions underlying reasoning and describe a technique that helps enumerate assumptions methodically. • — The result is an overall strategy for troubleshooting based on the progressive relaxation of underlying assumptions. The system can focus its efforts initially, yet will methodically expand its focus to include a broad range of faults. • — Finally, abstracting from our examples, we find that the concept of adjacency proves to be useful in understanding why some faults are especially difficult to diagnose and why multiple representations are useful.

Reasoning from first principles in electronic troubleshooting

Abstract: While expert systems have traditionally been built using large collections of rules based on empirical associations, interest has grown recently in the use of systems that reason “from first principles”, i.e. from an understanding of causality of the device being examined. Our work explores the use of such models in troubleshooting digital electronics. In discussing troubleshooting we show why the traditional approach—test generation—solves a different problem and we discuss a number of its practical shortcomings. We consider next the style of debugging known as discrepancy detection and demon-strate why it is a fundamental advance over traditional test generation. Further explor-ation, however, demonstrates that in its Standard form discrepancy detection encounters interesting limits in dealing with commonly known classes of faults. We suggest that the problem arises from a number of interesting implicit assumptions typically made when using the technique. In discussing how to repair the problems uncovered, we argue for the primacy of models of causal interaction, rather than the traditional fault models. We point out the importance of making these models explicit, separated from the troubleshooting mechanism, and retractable in much the same sense that inferences are retracted in current systems. We report on progress to date in implementing this approach and demonstrate the diagnosis of a bridge fault—a traditionally difficult problem—using our approach.