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Abduction in Logic Programming
"... Abduction in Logic Programming started in the late 80s, early 90s, in an attempt to extend logic programming into a framework suitable for a variety of problems in Artificial Intelligence and other areas of Computer Science. This paper aims to chart out the main developments of the field over th ..."
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Cited by 538 (74 self)
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Abduction in Logic Programming started in the late 80s, early 90s, in an attempt to extend logic programming into a framework suitable for a variety of problems in Artificial Intelligence and other areas of Computer Science. This paper aims to chart out the main developments of the field over the last ten years and to take a critical view of these developments from several perspectives: logical, epistemological, computational and suitability to application. The paper attempts to expose some of the challenges and prospects for the further development of the field.
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 298 (37 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...
The Complexity of LogicBased Abduction
, 1993
"... Abduction is an important form of nonmonotonic reasoning allowing one to find explanations for certain symptoms or manifestations. When the application domain is described by a logical theory, we speak about logicbased abduction. Candidates for abductive explanations are usually subjected to minima ..."
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Cited by 163 (26 self)
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Abduction is an important form of nonmonotonic reasoning allowing one to find explanations for certain symptoms or manifestations. When the application domain is described by a logical theory, we speak about logicbased abduction. Candidates for abductive explanations are usually subjected to minimality criteria such as subsetminimality, minimal cardinality, minimal weight, or minimality under prioritization of individual hypotheses. This paper presents a comprehensive complexity analysis of relevant decision and search problems related to abduction on propositional theories. Our results indicate that abduction is harder than deduction. In particular, we show that with the most basic forms of abduction the relevant decision problems are complete for complexity classes at the second level of the polynomial hierarchy, while the use of prioritization raises the complexity to the third level in certain cases.
Compiling A Default Reasoning System into Prolog
 New Generation Computing
, 1990
"... Artificial intelligence researchers have been designing representation systems for default and abductive reasoning. Logic Programming researchers have been working on techniques to improve the efficiency of Horn Clause deduction systems. This paper describes how one such default and abductive reason ..."
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Cited by 30 (4 self)
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Artificial intelligence researchers have been designing representation systems for default and abductive reasoning. Logic Programming researchers have been working on techniques to improve the efficiency of Horn Clause deduction systems. This paper describes how one such default and abductive reasoning system (namely Theorist) can be translated into Horn clauses (with negation as failure), so that we can use the clarity of abductive reasoning systems and the efficiency of Horn clause deduction systems. We thus show how advances in expressive power that artificial intelligence workers are working on can directly utilise advances in efficiency that logic programming researchers are working on. Actual code from a running system is given. 1 Introduction Many people in Artificial Intelligence have been working on default reasoning and abductive diagnosis systems [35, 20, 4, 29]. The systems implemented so far (eg., [1, 16, 12, 34, 32]) are only prototypes or have been developed in A Theo...
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 ...
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 23 (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.
A Spectrum of Definitions for Temporal ModelBased Diagnosis
 Artificial Intelligence
, 1998
"... Modelbased 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 timedependent in one way or another. Temporal MBD, however, is a difficult ta ..."
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Cited by 21 (6 self)
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Modelbased 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 timedependent 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 knowledgelevel characterization of the problem. In this paper we present a general characterization of temporal modelbased 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...
Representing diagnostic knowledge for probabilistic horn abduction
 Readings in modelbased diagnosis
, 1992
"... This paper presents a simple logical framework for abduction, with probabilities associated with hypotheses. The language is an extension to pure Prolog, and it has straightforward implementations using branch and bound search with either logicprogramming technology or ATMS technology. The main fo ..."
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Cited by 20 (6 self)
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This paper presents a simple logical framework for abduction, with probabilities associated with hypotheses. The language is an extension to pure Prolog, and it has straightforward implementations using branch and bound search with either logicprogramming technology or ATMS technology. The main focus of this paper is arguing for a form of representational adequacy of this very simple system for diagnostic reasoning. It is shown how it can represent modelbased knowledge, with and without faults, and with and without nonintermittency assumptions. It is also shown how this representation can represent any probabilistic knowledge representable in a Bayesian belief network. 1
Representing Diagnosis Knowledge
 Annals of Mathematics and Artificial Intelligence
, 1994
"... This paper considers the representation problem: namely how to go from an abstract problem to a formal representation of the problem. We consider this for two conceptions of logicbased diagnosis, namely abductive and consistencybased diagnosis. We show how to represent diagnostic problems that can ..."
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Cited by 18 (2 self)
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This paper considers the representation problem: namely how to go from an abstract problem to a formal representation of the problem. We consider this for two conceptions of logicbased diagnosis, namely abductive and consistencybased diagnosis. We show how to represent diagnostic problems that can be conceptualised causally in each of the frameworks, and show that both representations of the same problems give the same answers. This is a local transformation that allows for an expressive (albeit propositional) language for giving the constraints on what symptoms and causes can coexist, including nonstrict causation. This nonstrict causation can be represented in each framework without adding special reasoning constructs to either framework. This is presented as a starting point for a study of the representation problem in diagnosis, rather than as an end in itself. 1 Introduction This paper defines an abstract "knowledge representation" problem and considers the problem of represe...
The Independent Choice Logic and Beyond
"... Abstract. The Independent Choice Logic began in the early 90’s as a way to combine logic programming and probability into a coherent framework. The idea of the Independent Choice Logic is straightforward: there is a set of independent choices with a probability distribution over each choice, and a l ..."
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Cited by 18 (5 self)
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Abstract. The Independent Choice Logic began in the early 90’s as a way to combine logic programming and probability into a coherent framework. The idea of the Independent Choice Logic is straightforward: there is a set of independent choices with a probability distribution over each choice, and a logic program that gives the consequences of the choices. There is a measure over possible worlds that is defined by the probabilities of the independent choices, and what is true in each possible world is given by choices made in that world and the logic program. ICL is interesting because it is a simple, natural and expressive representation of rich probabilistic models. This paper gives an overview of the work done over the last decade and half, and points towards the considerable work ahead, particularly in the areas of lifted inference and the problems of existence and identity. 1