We propose a model

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.

This paper is the continuation of [1] in this volume. There we present a sceptical semantics which avoids contradiction for extended logic programs plus integrity contraints in the form of denials, based on the notion of optative hypotheses --an abductive approach. In this part we define a program revision method for removing contradiction from contradictory programs under WFSX, based on the notion of revisable hypotheses --a belief revision approach-- and show the equivalence between the contradiction avoidance semantics and the WFSX of revised programs obtained by contradiction removal. The motivation, as well as some preliminary definitions can be found in [1]. Proofs of all theorems are omitted for brevity, but exist in an extended version of this work.

The safety-critical nature of the application of knowledge-based systems to the field of medicine, demands the adoption of reliable engineering principles with a solid foundation for their construction. Logical languages with their inherent, precise notions of consistency, soundness and completeness offer such a foundation, thus promoting scrutinous engineering of medical knowledge. Moreover, logic techniques provide a powerful means for getting insight into the structure and meaning of medical knowledge used in medical problem solving. Unfortunately, logic is currently only used on a small scale for building practical medical knowledge-based systems. In this paper, the various approaches proposed in the literature are reviewed, and related to different types of knowledge and problem solving employed in the medical field. The appropriateness of logic for building medical knowledge-based expert systems is further motivated.

The paper provides a uniform representation of abductive reasoning in the logical framework of causal inference relations. The representation covers in a single framework not only traditional, ‘classical ’ forms of abduction, but also abductive reasoning in diagnosis, theories of actions and change, and abductive logic programming. 1

In this paper, diagnosis is viewed as a two-stage process: domain knowledge is rst interpreted in a diagnostic sense; next, observed ndings are interpreted with respect to this interpreted knowledge and a given hypothesis, yielding a diagnosis. A set-theoretical framework is briey discussed that captures this view on diagnosis; it is used to formalize various notions of diagnosis, those proposed in the literature included. Next, a theory of exible diagnosis, called renement diagnosis, is proposed and dened in terms of this framework. Relationships with notions of diagnosis known from the literature are investigated. Keywords & Phrases: model-based diagnosis, theory of diagnosis, formal methods. 1 Introduction In recent years, several theories of diagnosis have been developed, providing dierent foundations for diagnostic problem solving in intelligent systems. In particular, theories have been proposed which try to capture the structure of diagnosis. Diagnostic problem s...

. We apply to normal logic programs with integrity rules a contradiction removal approach, and use it to uniformly treat diagnosis and debugging, and as a matter of fact envisage programs as artifacts and fault-finding as debugging. Our originality resides in applying to such programs the principle that if an assumption leads to contradiction then it should be revised: assumptions are not A literals with no rules for A; contradiction is violation of an integrity rule; and revision consists in assuming A instead. Since revised assumptions may introduce fresh contradictions the revision process must be iterated. To do so we've devised an algorithm which is sound and complete. Our use of normal logic programs extends that of Horn programs made by Konolige, and so adds expressiveness to the causal part of his framework. Non-abnormalities are assumed rather than abduced, and are revised only if they result in contradiction; simple logic programming techniques achieve it. Keywords: Diagnosis...