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Abducing through negation as failure: stable models within the independent choice logic
 J. Log. Program
"... The independent choice logic (ICL) is part of a project to combine logic and decision/game theory into a coherent framework. The ICL has a simple possibleworlds semantics characterised by independent choices and an acyclic logic program that specifies the consequences of these choices. This paper g ..."
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Cited by 39 (8 self)
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The independent choice logic (ICL) is part of a project to combine logic and decision/game theory into a coherent framework. The ICL has a simple possibleworlds semantics characterised by independent choices and an acyclic logic program that specifies the consequences of these choices. This paper gives an abductive characterization of the ICL. The ICL is defined modeltheoretically, but we show that it is naturally abductive: the set of explanations of a proposition g is a concise description of the worlds in which g is true. We give an algorithm for computing explanations and show it is sound and complete with respect to the possibleworlds semantics. What is unique about this approach is that the explanations of the negation of g can be derived from the explanations of g. The use of probabilities over choices in this framework and going beyond acyclic logic programs are also discussed.
Symbolic Diagnosis and its Formalisation
 The Knowledge Engineering Review
, 1997
"... 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 knowledgebased syste ..."
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Cited by 24 (6 self)
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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 knowledgebased systems for the purpose of diagnosis, often referred to as modelbased 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.
Analysis of Notions of Diagnosis
, 1998
"... 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 modelbased diagnosis, which is used in diagnostic ..."
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Cited by 23 (2 self)
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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 modelbased 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 settheoretical 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 settheoretical framework offers a simple, yet powerful tool for comparing existing notions of diagnosis, as well as for proposing new notions ...
An Intelligent System for Pacemaker Reprogramming
 Artificial Intelligence in Medicine
, 1999
"... The process of reprogramming a cardiac pacemaker can be described in terms similar to those used for describing diagnostic problem solving. In this paper, the process of reprogramming a pacemaker is formalised as a special form of abductive diagnostic reasoning, where observable findings are interpr ..."
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Cited by 8 (0 self)
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The process of reprogramming a cardiac pacemaker can be described in terms similar to those used for describing diagnostic problem solving. In this paper, the process of reprogramming a pacemaker is formalised as a special form of abductive diagnostic reasoning, where observable findings are interpreted with respect to results obtained from diagnostic tests. The dynamics of this process is cast as a diagnostic strategy, where information is gathered in a structured fashion. This abductive theory of pacemaker reprogramming has been used as the basis for an actual system that in its present form is capable of assisting cardiologists in dealing with problems in atrial sensing and pacing. The performance of the system has been evaluated using data from actual patients.
Learning, Bayesian Probability, Graphical Models, and Abduction
 Abduction and Induction: Essays on their Relation and Integration, Chapter 10
, 1998
"... In this chapter I review Bayesian statistics as used for induction and relate it to logicbased abduction. Much reasoning under uncertainty, including induction, is based on Bayes' rule. Bayes' rule is interesting precisely because it provides a mechanism for abduction. I review work of Bu ..."
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Cited by 7 (0 self)
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In this chapter I review Bayesian statistics as used for induction and relate it to logicbased abduction. Much reasoning under uncertainty, including induction, is based on Bayes' rule. Bayes' rule is interesting precisely because it provides a mechanism for abduction. I review work of Buntine that argues that much of the work on Bayesian learning can be best viewed in terms of graphical models such as Bayesian networks, and review previous work of Poole that relates Bayesian networks to logicbased abduction. This lets us see how much of the work on induction can be viewed in terms of logicbased abduction. I then explore what this means for extending logicbased abduction to richer representations, such as learning decision trees with probabilities at the leaves. Much of this paper is tutorial in nature; both the probabilistic and logicbased notions of abduction and induction are introduced and motivated. 1 Introduction This paper explores the relationship between learning (induct...
Representing and Reasoning about Concurrent Actions with Abductive Logic Programs
, 1997
"... In this paper we extend Gelfond and Lifschitz' action description language A with concurrent actions and observation propositions to describe the predicted behaviour of domains of (concurrent) actions and actually observed behaviour, respectively, without requiring that the actually observed be ..."
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Cited by 6 (1 self)
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In this paper we extend Gelfond and Lifschitz' action description language A with concurrent actions and observation propositions to describe the predicted behaviour of domains of (concurrent) actions and actually observed behaviour, respectively, without requiring that the actually observed behaviour of a domain of actions be consistent with its predicted behaviour. We present a translation from domain descriptions and observations in the new action language to abductive normal logic programs. The translation is shown to be both sound and complete. From the standpoint of modelbased diagnosis, in particular, we discuss the temporal explanation of inferring actions from fluent changes at two different levels, namely, at the domain description level and at the abductive logic programming level. The method is applicable to the temporal projection problem with incomplete information, as well as to the temporal explanation of inferring actions from fluent changes.
A Model Counting Characterization of Diagnoses
, 2002
"... Given the description of a physical system in one of several forms (a set of constraints, Bayesian network etc.) and a set of observations made, the task of modelbased diagnosis is to find a suitable assignment to the modes of behavior of individual components (this notion can also be extended ..."
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Cited by 5 (1 self)
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Given the description of a physical system in one of several forms (a set of constraints, Bayesian network etc.) and a set of observations made, the task of modelbased diagnosis is to find a suitable assignment to the modes of behavior of individual components (this notion can also be extended to handle transitions and dynamic systems [Kurien and Nayak, 2000] . Many formalisms have been proposed in the past to characterize diagnoses and systems. These include consistencybased diagnosis, fault models, abduction, combinatorial optimization, Bayesian model selection etc. Different approaches are apparently well suited for different applications and representational forms in which the system description is available. In this paper, we provide a unifying theme behind all these approaches based on the notion of model counting.
A Causal Theory of Abduction
 in &quot;7th Int. Symposium on Logical Formalizations of Common Sense Reasoning
"... 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, ..."
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Cited by 5 (0 self)
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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
A Theory of Diagnosis as Hypothesis Refinement
, 1996
"... In this paper, diagnosis is viewed as a twostage 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 settheoretical framework is briey discussed that ..."
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Cited by 2 (1 self)
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In this paper, diagnosis is viewed as a twostage 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 settheoretical 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: modelbased 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...
Abductive and ConsistencyBased Diagnosis Revisited: a Modeling Perspective
 Proc. of the 8th International Workshop on NonMonotonic Reasoning, NMR'2000
, 2000
"... Diagnostic reasoning has been characterized logically as consistencybased reasoning or abductive reasoning. Previous analyses in the literature have shown, on the one hand, that choosing the (in general more restrictive) abductive definition may be appropriate or not, depending on the content ..."
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Cited by 2 (0 self)
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Diagnostic reasoning has been characterized logically as consistencybased reasoning or abductive reasoning. Previous analyses in the literature have shown, on the one hand, that choosing the (in general more restrictive) abductive definition may be appropriate or not, depending on the content of the knowledge base (Console & Torasso 1991), and, on the other hand, that, depending on the choice of the definition the same knowledge should be expressed in different form (Poole 1994). Since in ModelBased Diagnosis a major problem is finding the right way of abstracting the behavior of the system to be modeled, this paper discusses the relation between modeling, and in particular abstraction in the model, and the notion of diagnosis. Introduction Several characterizations have been given for ModelBased Diagnosis (Hamscher, Console, & J. de Kleer 1992). All approaches assume that a model of the system to be diagnosed is available: either a model of the correct behavior of the...