The problem of abduction can be characterized as finding the best explanation of a set of data. In this paper we focus on one type of abduction in which the best explanation is the most plausible combination of hypotheses that explains all the data. We then present several computational complexity results demonstrating that this type of abduction is intractable (NP-hard) in general. In particular, choosing between incompatible hypotheses, reasoning about cancellation effects among hypotheses, and satisfying the maximum plausibility requirement are major factors leading to intractability. We also identify a tractable, but restricted, class of abduction problems. Thanks to B. Chandrasekaran, Ashok Goel, Jack Smith, and Jon Sticklen for their comments on the numerous versions of this paper. The referees have also made a substantial contribution. Any remaining errors are our responsibility, of course. This research has been supported in part by the National Library of Medicine, grant LM-...

Mostly, papers on problem-solving methods focus on the description of reasoning strategies and discuss their underlying assumptions as a side aspect. We take a complementary point of view and focus on these underlying assumptions as they play three important roles: first, assumptions are necessary to characterise the precise competence of a problem-solving method in terms of the tasks that can be solved by it, and in terms of the domain knowledge that is required by it. Second, assumptions are necessary to enable tractable problem solving for complex problems. Third, assumptions are necessary for appropriate interaction of the problem solver with its environment. Their introduction and refinement can be used to develop new problem-solving methods, or to adapt existing ones according to task and domain-specific circumstances of a given application. For this purpose, one requires a framework for dealing with these assumptions. This paper makes a step in this direction by sum...

We extend Reiter's general theory of model-based diagnosis [13] to a theory of reconfiguration. The generality of Reiter's theory readily supports an extension in which the problem of reconfiguration is viewed as a close analogue of the problem of diagnosis. Using a reconfiguration predicate rcfg analogous to the abnormality predicate ab, we formulate a strategy for reconfiguration by transforming that for diagnosis. A benefit of this approach is that algorithms for diagnosis can be exploited as algorithms for reconfiguration, thereby promoting an integrated approach to fault detection, identification, and reconfiguration. The research reported here was supported by the National Aeronautics and Space Administration under Contract No. NAS1-18969. 1 1 Introduction Automated diagnosis has been one of the most fruitful applications of AI. However, while it is important to identify the faults in a malfunctioning system, the real problem is usually to repair the system so that it...

A main obstacle for building large diagnosis systems is the problem of deciding when to use which inference pattern or representation. If diagnostic actions such as changing representations or applying specific inference patterns are understood in terms of change of working hypotheses, the control problem becomes a belief revision problem: when to adopt or drop beliefs. Our approach proceeds in two steps. First, we adopt the principle of informational economy as kind of a law of inertia for diagnostic processes. It proposes candidates for revised belief states. In a second step we employ specific diagnostic knowledge to actually choose the next belief state. 1. Introduction In recent years reasoning about structure and function of physical systems for the purpose of diagnosis has seen a dramatic increase in activities. A considerable set of new exciting results have emerged from these: . modelling issues such as representation and use of temporal aspects [Hamscher 91], models of dif...

We present IDA --- an Incremental Diagnostic Algorithm which computes minimal diagnoses from diagnoses, and not from conflicts. As a consequence of this, and by using different models, one can control the computational complexity. In particular, we show that by using a model of the normal behavior, the worst-case complexity of the algorithm to compute the k + 1-st minimal diagnosis is O(n 2k ), where n is the number of components. On the practical side, an experimental evaluation indicates that the algorithm can efficiently diagnose devices consisting of a few thousand components. We propose to use a hierarchy of models: first a structural model to compute all minimal diagnoses, then a normal behavior model to find the additional diagnoses if needed, and only then a fault model for their verification. IDA separates model interpretation from the search for minimal diagnoses in the sense that the model interpreter is replaceable. In particular, we show that in some domains it is advan...

Repairing violations of integrity constraints in databases can be seen as an interleaving diagnostic/repair process. In this paper we introduce a new approach on repairing constraint violations by adopting existing techniques from model-based diagnosis. Violations of integrity constraints observed in an inconsistent database state are diagnosed and repair actions are deduced from diagnoses. By interleaving diagnosing constraint violations and performing repair actions, transactions are computed which restore the consistency of the database.

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...

Prime implicants/implicates (PIs) have been shown to be a useful tool in several problem domains. In Model-Based Diagnosis (MBD), [de Kleer et al. 90] have used PIs to characterize diagnoses. We present a PI generation algorithm which, although based on the general SE-tree-based search framework, is effectively an improvement of a particular PI generation algorithm proposed by [Slagle et al. 70]. The improvement is achieved via a decomposition tactic which is boosted by the SE-tree-based framework. The new algorithm is also more flexible in a number of ways. We present empirical results comparing the new algorithm to the old one, as well as to current PI generation algorithms. 1 Introduction Prime implicates/implicants (PIs) were a topic of great interest to researchers in the early days of computer science, in part because of their use in procedures for boolean function minimization [Quine 52]. A number of algorithms were developed, including [Quine 52], [Karnaugh 53], [McCluskey 56]...

. Problem-solving methods are means to describe the inference process of knowledge-based systems. During the last years, a number of these problemsolving methods have been identified that can be reused for building new systems. However, problem-solving methods require specific types of domain knowledge and introduce specific restrictions on the tasks that can be solved by them. These requirements and restrictions are assumptions that play a key role in reusing problem-solving methods, in acquiring domain knowledge, and in defining the problem that can be tackled by the knowledge-based systems. In the paper, we discuss the different roles, assumptions play in the development process of knowledge-based systems and provide a survey of assumptions used by diagnostic problem solving. We show how such assumptions introduce target and bias for goal-driven machine learning and knowledge discovery techniques. 1 INTRODUCTION During the last years, Problem-solving methods (PSMs) have become quit...

In recent years reasoning about structure and function of physical systems for the purpose of diagnosis has seen a dramatic increase in activities. New exciting results concerning modelling issues, diagnostic inference patterns and inferential power have emerged. A state of the art diagnosis agent now has a considerable toolset at hand. A main obstacle for building large diagnosis systems, however, remains. How can we control when to use which inference pattern or representation ? We argue that the actions available to a diagnosis agent can be understood in terms of change of working hypotheses. The control problem then becomes a belief revision problem: when to adopt or drop beliefs. Our approach proceeds in two steps. First, we adopt the principle of informational economy from [Grdenfors 88] as kind of a law of inertia for diagnostic processes, that helps us identify candidates for revised belief states. In a second step we employ specific diagnostic knowledge to actually choose th...