Suppose one is given a description of a system, together with an observation of the system's behaviour which conflicts with the way the system is meant to behave. The diagnostic problem is to determine those components of the system which, when assumed to be functioning abnormally, will explain the discrepancy between the observed and correct system behaviour. We propose a general theory for this problem. The theory requires only that the system be described in a suitable logic. Moreover, there are many such suitable logics, e.g. first-order, temporal, dynamic, etc. As a result, the theory accommodates diagnostic reasoning in a wide variety of practical settings, including digital and analogue circuits, medicine, and database updates. The theory leads to an algorithm for computing all diagnoses, and to various results concerning principles of measurement for discriminating among competing diagnoses. Finally, the theory reveals close connections between diagnostic reasoning and nonmonotonic reasoning.

Model-based reasoning about a system requires an explicit representation of the system's components and their connections. Diagnosing such a system consists of locating those components whose abnormal behavior accounts for the faulty system behavior. In order to increase the efficiency of model-based diagnosis, we propose a model representation at several levels of detail, and define three refinement (abstraction) operators. We specify formal conditions that have to be satisfied by the hierarchical representation, and emphasize that the multi-level scheme is independent of any particular single-level model representation. The hierarchical diagnostic algorithm which we define turns out to be very general. We show that it emulates the bisection method, and can be used for hierarchical constraint satisfaction. We apply the hierarchical modeling principle and diagnostic algorithm to a medium-scale medical problem. The performance of a four-level qualitative model of the heart is compared t...

Various formal theories have been proposed in the literature to capture the notions of diagnosis underlying diagnostic programs. Examples of such notions are: heuristic classification, which is used in systems incorporating empirical knowledge, and model-based diagnosis, which is used in diagnostic systems based on detailed domain models. Typically, such domain models include knowledge of causal, structural, and functional interactions among modelled objects. In this paper, a new set-theoretical framework for the analysis of diagnosis is presented. Basically, the framework distinguishes between `evidence functions', which characterize the net impact of knowledge bases for purposes of diagnosis, and `notions of diagnosis', which define how evidence functions are to be used to map findings observed for a problem case to diagnostic solutions. This set-theoretical framework offers a simple, yet powerful tool for comparing existing notions of diagnosis, as well as for proposing new notions ...

Diagnosis was among the first subjects investigated when digital computers became available. It still remains an important research area, in which several new developments have taken place in the last decade. One of these new developments is the use of detailed domain models in knowledge-based systems for the purpose of diagnosis, often referred to as model-based diagnosis. Typically, such models embody knowledge of the normal or abnormal structure and behaviour of the modelled objects in a domain. Models of the structure and workings of technical devices, and causal models of disease processes in medicine are two examples. In this article, the most important notions of diagnosis and their formalisation are reviewed and brought in perspective. In addition, attention is focused on a number of general frameworks of diagnosis, which offer sufficient flexibility for expressing several types of diagnosis.

The mathematical foundations of model-based diagnostics or diagnosis from first principles have been laid by Reiter [31]. In this paper we extend Reiter’s ideas of model-based diagnostics by introducing probabilities into Reiter’s framework. This is done in a mathematically sound and precise way which allows one to compute the posterior probability that a certain component is not working correctly given some observations of the system. A straightforward computation of these probabilities is not efficient and in this paper we propose a new method to solve this problem. Our method is logic-based and borrows ideas from assumption-based reasoning and ATMS. We show how it is possible to determine arguments in favor of the hypothesis that a certain group of components is not working correctly. These arguments represent the symbolic or qualitative aspect of the diagnosis process. Then they are used to derive a quantitative or numerical aspect represented by the posterior probabilities. Using two new theorems about the relation between Reiter’s notion of conflict and our notion of argument, we prove that our so-called degree of support is nothing but the posterior probability that we are looking for. Furthermore, a model where each component may have more than two different operating modes is discussed and a new algorithm to compute posterior probabilities in this case is presented. Key words: Model-based diagnostics; Assumption-based reasoning; ATMS;

We propose to regard a diagnostic system as an ordered logic theory, i.e. a partially ordered set of clauses where smaller rules carry more preference.

Diagnosis is an important application area of Artificial Intelligence. First generation expert diagnostic systems had exhibited difficulties which motivated the development of model-based reasoning techniques. Model-based diagnosis is the activity of locating malfunctioning components of a system solely on the basis of its structure and behavior. The paper gives a brief overview of the main concepts, problems, and research results in this area. 1 Introduction Diagnosis is one of the earliest areas in which application of Artificial Intelligence techniques was attempted. The diagnosis of a system which behaves abnormally consists of locating those subsystems whose abnormal behavior accounts for the observed behavior. For example, a system being diagnosed might be a mechanical device exhibiting malfunction, or a human patient. There are two fundamentally different approaches to diagnostic reasoning. In the first, heuristic approach, one attempts to codify diagnostic rules of thumb and p...

Model-based diagnosis concerns using a model of the structure and behaviour of a system or device in order to establish why the system or device is malfunctioning. Traditionally, little attention has been given to the problem of dealing with uncertainty in model-based diagnosis. Given the fact that determining a diagnosis for a problem almost always involves uncertainty, this situation is not entirely satisfactory. This paper builds upon and extends previous work in model-based diagnosis by supplementing the well-known model-based framework with mathematically sound ways for dealing with uncertainty.

Technical systems are in general not guaranteed to work correctly. They are more or less reliable. One main problem for technical systems is the computation of the reliability of a system. A second main problem is the problem of diagnostic. In fact, these problems are in some sense dual to each other.

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