Appeared in: 12 th International Workshop on Principles of Diagnosis, 2001. G + DE- The Generalized Diagnosis Engine (original) (raw)
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Consistency-based diagnosis is one of the most widely used approaches to model-based diagnosis within the artificial intelligence community. It is usually carried out through an iterative cycle of behavior prediction, conflict detection, candidate generation, and candidate refinement. In that process conflict detection has proven to be a nontrivial step from the theoretical point of view. For this reason, many approaches to consistency-based diagnosis have relied upon some kind of dependency-recording. These techniques have had different problems, specially when they were applied to diagnose dynamic systems. Recently, offline dependency compilation has established itself as a suitable alternative approach to online dependency-recording. In this paper we propose the possible conflict concept as a compilation technique for consistency-based diagnosis. Each possible conflict represents a subsystem within system description containing minimal analytical redundancy and being capable to become a conflict. Moreover, the whole set of possible conflicts can be computed offline with no model evaluation. Once we have formalized the possible conflict concept, we explain how possible conflicts can be used in the consistency-based diagnosis framework, and how this concept can be easily extended to diagnose dynamic systems. Finally, we analyze its relation to conflicts in the general diagnosis engine (GDE) framework and compare possible conflicts with other compilation techniques, especially with analytical redundancy relations (ARRs) obtained through structural analysis. Based on results from these comparisons we provide additional insights in the work carried out within the BRIDGE community to provide a common framework for model-based diagnosis for both artificial intelligence and control engineering approaches
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Model-based diagnosis (MBD) is a fundamental approach for automated diagnosis, in which a model of the diagnosed system is used to identify abnormally behaving system components. As systems become large-scale and more complex, their models also grow in size, and consequently applying MBD becomes more computationally challenging. Structural abstraction was shown to be effective in scaling up MBD algorithms to larger systems. However, past work on using structural abstraction in MBD assumed, either explicitly or implicitly, a weak fault model (WFM), i.e., that the system model specify only the normal behavior of the system components. Therefore, the resulting diagnoses can be inconsistent with existing knowledge about how the system behaves when it is abnormal. System models that contain such information are said to have a strong fault model (SFM). In this work, we show that a standard approach for using cones abstraction , a form of structural abstraction that was shown to be useful for directional systems, does not work for systems with a SFM. Then, we propose several sound and complete algorithms that can use a cones abstraction effectively to diagnose systems with a SFM. Some of these algorithms use Machine Learning techniques to predict which cones will not be useful in the diagnosis process and should be discarded. Empirical evaluation on benchmark systems that model Boolean circuits shows that our algorithms are very effective in practice. The empirical evaluation also sheds light on how various system properties affect the comparative performance of the proposed algorithm.
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In this paper we propose a new characterization of model-based diagnosis based on process algebras, a framework which is widely used in several areas of computer science. We show that process algebras provide a powerful modelling language which allows us to capture, in an uniform way, different types of models of physical systems, including models of time-varying and dynamic behavior. Then we provide a characterization of diagnosis which is equivalent to the "classical" abductive one. This suggests new interesting opportunities for research on relations between modelbased reasoning and process algebras. (L. Console), picardi@di.unito.it (C. Picardi), marina@di.unito.it (M. Ribaudo). 0004-3702/02/$ -see front matter 2002 Elsevier Science B.V. All rights reserved. PII: S 0 0 0 4 -3 7 0 2 ( 0 2 ) 0 0 2 9 2 -8 20 L. Console et al. / Artificial Intelligence 142 (2002) 19-51 (e.g., computational complexity [13,57] or diagnosability ). Moreover, they provided conceptual tools for analyzing application problems and domains and for relating them to the different approaches for modelling and problem solving; as a result, they have been used for defining frameworks which provide guidelines for studying which approaches to modelling and diagnosis are suitable for a given application problem or domain (e.g., see ). Together with the applications, the foundational works contributed to singling out new open problems and opportunities for research. Last, but not least, they contributed to the creation of bridges between model-based reasoning and other areas of artificial intelligence and computer science such as logical and non-monotonic reasoning, probabilistic reasoning, machine learning, control theory, to mention only some of them.
G+DE - The Generalized Diagnosis Engine
2001
The existing theory of consistency-based diagnosis and its implementations have proven successful in a number of technical applications. However, they turn out to be inherently limited to a very specific class of systems to be diagnosed: They are tailored for artifacts consisting of components in a fixed structure, and they are aimed at a particular kind of diagnosis and repair,
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Our work in model-based analysis, diagnosis, and therapy of environmental systems reveals principled limitations in standard theories and techniques of model-based diagnosis. It becomes evident that they are (implicitly) tailored for di- agnosing artifacts based on component-oriented modeling. When we deal with a natural or technical system that lacks a static structure comprising a fixed set of components and that we prefer to model as a collection of processes, a num- ber of conditions and goals of the diagnosis process change. Processes do not become faulty like components, and often, the goal is not to find the culprit among the known compo- nents of a system, but to identify additional objects that were not part of the initial system description, for instance toxic waste. We propose a revision of the traditional theo- ries of diagnosis from first principles. The goal is to make it more general in terms of the class of problems to be ad- dressed and more specific by proposing an...
On the efficiency of logic-based diagnosis
Proceedings of the third international conference on Industrial and engineering applications of artificial intelligence and expert systems - IEA/AIE '90, 1990
Diagnosis is a problem in which one must explain the discrepancy between the observed and correct system behavior by assuming some component (possibly multiple components) of the system is functioning abnormally. A diagnostic reasoning system must deal with two issues concerning computational efficiency. The first is efficient search in a complex space for all possible diagnoses for a given set of observations about the faulty system. The second is efficient discrimination amongst multiple competing diagnoses. We consider the problem of diagnosis from the perspective of the Theorist hypothetical reasoning framework which provides a simple and intuitive diagnostic method. We propose an extension to the Theorist framework that modifies the consistency check mechanism to incrementally compute inconsisfencies, sometimes referred to as nogoods, and to identify crucial liter& to perform tests for discriminating among competing diagnoses. A prototype is implimented in Cprolog and its empirical efficiency is shown by considering examples from two different domains of diagnosis. 1 Introduction Diagnosis is a problem in which one must explain discrepancies between observed and correct system behavior. Because of the ubiquity of this problem in real world situations, including failure of a nuclear plant, medical diagnosis, faults in an electronic circuit, etc., Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission.