A spectrum of definitions for temporal model-based diagnosis

• Published in: Artificial intelligence Vol. 102; no. 1; pp. 39 - 79
• Main Authors: Brusoni, Vittorio, Console, Luca, Terenziani, Paolo, Dupré, Daniele Theseider
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.1998
• Subjects: Artificial intelligence; Diagnosis; Formal languages; Mathematical models; Mathematical techniques; Model-based reasoning; Reasoning; Temporal dimension; Temporal reasoning; Model-based reasoning; Temporal reasoning; Diagnosis
• ISSN: 0004-3702; 1872-7921
• DOI: 10.1016/S0004-3702(98)00044-7

Model-based diagnosis (MBD) tackles the problem of troubleshooting systems starting from a description of their structure and function (or behavior). Time is a fundamental dimension in MBD: the behavior of most systems is time-dependent in one way or another. Temporal MBD, however, is a difficult task and indeed many simplifying assumptions have been adopted in the various approaches in the literature. These assumptions concern different aspects such as the type and granularity of the temporal phenomena being modeled, the definition of diagnosis, the ontology for time being adopted. Unlike the atemporal case, moreover, there is no general “theory” of temporal MBD which can be used as a knowledge-level characterization of the problem. In this paper we present a general characterization of temporal model-based diagnosis. We distinguish between different temporal phenomena that can be taken into account in diagnosis and we introduce a modeling language which can capture all such phenomena. Given a suitable logical semantics for such a modeling language, we introduce a general characterization of the notions of diagnostic problem and explanation, showing that in the temporal case these definitions involve different parameters. Different choices for the parameters lead to different approaches to temporal diagnosis. We define a framework in which different dimensions for temporal model-based diagnosis can be analyzed at the knowledge level, pointing out which are the alternatives along each dimension and showing in which cases each one of these alternatives is adequate. In the final part of the paper we show how various approaches in the literature can be classified within our framework. In this way, we propose some guidelines to choose which approach best fits a given application problem.