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148
A Theory of Diagnosis from First Principles
 ARTIFICIAL INTELLIGENCE
, 1987
"... 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 ..."
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Cited by 947 (5 self)
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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. firstorder, 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.
Probabilistic Horn abduction and Bayesian networks
 Artificial Intelligence
, 1993
"... This paper presents a simple framework for Hornclause abduction, with probabilities associated with hypotheses. The framework incorporates assumptions about the rule base and independence assumptions amongst hypotheses. It is shown how any probabilistic knowledge representable in a discrete Bayesia ..."
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Cited by 305 (38 self)
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This paper presents a simple framework for Hornclause abduction, with probabilities associated with hypotheses. The framework incorporates assumptions about the rule base and independence assumptions amongst hypotheses. It is shown how any probabilistic knowledge representable in a discrete Bayesian belief network can be represented in this framework. The main contribution is in finding a relationship between logical and probabilistic notions of evidential reasoning. This provides a useful representation language in its own right, providing a compromise between heuristic and epistemic adequacy. It also shows how Bayesian networks can be extended beyond a propositional language. This paper also shows how a language with only (unconditionally) independent hypotheses can represent any probabilistic knowledge, and argues that it is better to invent new hypotheses to explain dependence rather than having to worry about dependence in the language. Scholar, Canadian Institute for Advanced...
Decision Theory in Expert Systems and Artificial Intelligence
 International Journal of Approximate Reasoning
, 1988
"... Despite their different perspectives, artificial intelligence (AI) and the disciplines of decision science have common roots and strive for similar goals. This paper surveys the potential for addressing problems in representation, inference, knowledge engineering, and explanation within the decision ..."
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Cited by 95 (18 self)
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Despite their different perspectives, artificial intelligence (AI) and the disciplines of decision science have common roots and strive for similar goals. This paper surveys the potential for addressing problems in representation, inference, knowledge engineering, and explanation within the decisiontheoretic framework. Recent analyses of the restrictions of several traditional AI reasoning techniques, coupled with the development of more tractable and expressive decisiontheoretic representation and inference strategies, have stimulated renewed interest in decision theory and decision analysis. We describe early experience with simple probabilistic schemes for automated reasoning, review the dominant expertsystem paradigm, and survey some recent research at the crossroads of AI and decision science. In particular, we present the belief network and influence diagram representations. Finally, we discuss issues that have not been studied in detail within the expertsystems sett...
Normality and Faults in LogicBased Diagnosis
"... Is there one logical definition of diagnosis? In this paper I argue that the answer to this question is "no". This paper is about the pragmatics of using logic for diagnosis; we show how two popular proposals for using logic for diagnosis, (namely abductive and consistencybased approaches ..."
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Cited by 88 (6 self)
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Is there one logical definition of diagnosis? In this paper I argue that the answer to this question is "no". This paper is about the pragmatics of using logic for diagnosis; we show how two popular proposals for using logic for diagnosis, (namely abductive and consistencybased approaches) can be used to solve diagnostic tasks. The cases with only knowledge about how normal components work (any deviation being an error) and where there are fault models (we try to find a covering of the observations) are considered as well as the continuum between. The result is that there are two fundamentally different, but equally powerful diagnostic paradigms. They require different knowledge about the world, and different ways to think about a domain. This result indicates that there may not be an axiomatisation of a domain that is independent of how the knowledge is to be used.
DecisionTheoretic Troubleshooting
 Communications of the ACM
, 1995
"... this paper, we develop a diagnostic procedure that not only seeks to identify the most likely causes of a malfunction, but also generates a plan of action for repair. This plan consists of repairing or replacing individual components of a composite device or system, as well as making observations o ..."
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Cited by 79 (4 self)
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this paper, we develop a diagnostic procedure that not only seeks to identify the most likely causes of a malfunction, but also generates a plan of action for repair. This plan consists of repairing or replacing individual components of a composite device or system, as well as making observations or tests. We and others call this process troubleshooting [3]. Optimal Troubleshooting and Decision Trees
Towards understanding and harnessing the potential of clause learning
 Journal of Artificial Intelligence Research
, 2004
"... Efficient implementations of DPLL with the addition of clause learning are the fastest complete Boolean satisfiability solvers and can handle many significant realworld problems, such as verification, planning and design. Despite its importance, little is known of the ultimate strengths and limitat ..."
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Cited by 77 (10 self)
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Efficient implementations of DPLL with the addition of clause learning are the fastest complete Boolean satisfiability solvers and can handle many significant realworld problems, such as verification, planning and design. Despite its importance, little is known of the ultimate strengths and limitations of the technique. This paper presents the first precise characterization of clause learning as a proof system (CL), and begins the task of understanding its power by relating it to the wellstudied resolution proof system. In particular, we show that with a new learning scheme, CL can provide exponentially shorter proofs than many proper refinements of general resolution (RES) satisfying a natural property. These include regular and DavisPutnam resolution, which are already known to be much stronger than ordinary DPLL. We also show that a slight variant of CL with unlimited restarts is as powerful as RES itself. Translating these analytical results to practice, however, presents a challenge because of the nondeterministic nature of clause learning algorithms. We propose a novel way of exploiting the underlying problem structure, in the form of a high level problem description such as a graph or PDDL specification, to guide clause learning algorithms toward faster solutions. We show that this leads to exponential speedups on grid and randomized pebbling problems, as well as substantial improvements on certain ordering formulas. 1.
Using Incomplete Quantitative Knowledge in Qualitative Reasoning
 In Proc. of the Sixth National Conference on Artificial Intelligence
, 1988
"... Incomplete knowledge of the structure of mechanisms is an important fact of life in reasoning, commonsense or expert, about the physical world. Qualitative simulation captures an important kind of incomplete, ordinal, knowledge, and predicts the set of qualitatively possible behaviors of a mechanism ..."
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Cited by 74 (17 self)
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Incomplete knowledge of the structure of mechanisms is an important fact of life in reasoning, commonsense or expert, about the physical world. Qualitative simulation captures an important kind of incomplete, ordinal, knowledge, and predicts the set of qualitatively possible behaviors of a mechanism, given a qualitative description of its structure and initial state. However, one frequently has quantitative knowledge as well as qualitative, though seldom enough to specify a numerical simulation.
A Methodology for Using a Default and Abductive Reasoning System
, 1994
"... This paper investigates two different activities that involve making assumptions: predicting what one expects to be true and explaining observations. In a companion paper, an architecture for both prediction and explanation is proposed and an implementation is outlined. In this paper, we show how su ..."
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Cited by 61 (10 self)
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This paper investigates two different activities that involve making assumptions: predicting what one expects to be true and explaining observations. In a companion paper, an architecture for both prediction and explanation is proposed and an implementation is outlined. In this paper, we show how such a hypothetical reasoning system can be used to solve recognition, diagnostic and prediction problems. As part of this is the assumption that the default reasoner must be "programmed" to get the right answer and it is not just a matter of "stating what is true" and hoping the system will magically find the right answer. A number of distinctions have been found in practice to be important: between predicting whether something is expected to be true versus explaining why it is true; and between conventional defaults (assumptions as a communication convention), normality defaults (assumed for expediency) and conjectures (assumed only if there is evidence). The effects of these distinctions on...
Multivalued Logics: A Uniform Approach to Inference in Artificial Intelligence
 Computational Intelligence
, 1988
"... This paper describes a uniform formalization of much of the current work in AI on inference systems. We show that many of these systems, including firstorder theorem provers, assumptionbased truth maintenance systems (atms's) and unimplemented formal systems such as default logic or circumscr ..."
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Cited by 57 (0 self)
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This paper describes a uniform formalization of much of the current work in AI on inference systems. We show that many of these systems, including firstorder theorem provers, assumptionbased truth maintenance systems (atms's) and unimplemented formal systems such as default logic or circumscription can be subsumed under a single general framework. We begin by defining this framework, which is based on a mathematical structure known as a bilattice. We present a formal definition of inference using this structure, and show that this definition generalizes work involving atms's and some simple nonmonotonic logics. Following the theoretical description, we describe a constructive approach to inference in this setting; the resulting generalization of both conventional inference and atms's is achieved without incurring any substantial computational overhead. We show that our approach can also be used to implement a default reasoner, and discuss a combination of default and atms methods th...