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On The Relationship Between Abduction And Deduction
, 1991
"... this paper is at analyzing from various points of view the relationships betwee ..."
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Cited by 174 (9 self)
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this paper is at analyzing from various points of view the relationships betwee
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 27 (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 24 (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...
A Unifying View for Logic Programming with NonMonotonic Reasoning
, 1997
"... We provide a simple formulation of a framework where some extensions of logic programming with nonmonotonic reasoning are treated uniformly, namely two kinds of negation and abduction. The resulting semantics is purely modeltheoretic, and gives meaning to any noncontradictory abductive logic pr ..."
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Cited by 18 (11 self)
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We provide a simple formulation of a framework where some extensions of logic programming with nonmonotonic reasoning are treated uniformly, namely two kinds of negation and abduction. The resulting semantics is purely modeltheoretic, and gives meaning to any noncontradictory abductive logic program. Moreover, it embeds and generalizes some existing semantics which deal with negation and abduction. The framework is equipped with a correct topdown proof procedure. Keywords: Programming languages, Logic programming, Nonmonotonic reasoning, Negation, Abduction. Dipartimento di Informatica, Universit`a di Pisa, Corso Italia 40, Pisa, Italy. brogi@di.unipi.it y DEIS, Universit`a di Bologna, Viale Risorgimento 2, 40136 Bologna, Italy. elamma@deis.unibo.it z Dipartimento di Informatica, Universit`a di Pisa, Corso Italia 40, Pisa, Italy. paolo@di.unipi.it x DEIS, Universit`a di Ferrara, Via Saragat, 41100 Ferrara, Italy. pmello@ing.unife.it Contents 1 Introduction and Motiva...
Representing Bayesian networks within probabilistic Horn abduction
 In Proc. Seventh Conf. on Uncertainty in Artificial Intelligence
, 1991
"... This paper presents a simple framework for Hornclause abduction, with probabilities associated with hypotheses. It is shown how this representation can represent any probabilistic knowledge representable in a Bayesian belief network. The main contributions are in finding a relationship between logic ..."
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Cited by 18 (4 self)
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This paper presents a simple framework for Hornclause abduction, with probabilities associated with hypotheses. It is shown how this representation can represent any probabilistic knowledge representable in a Bayesian belief network. The main contributions are in finding a relationship between logical and probabilistic notions of evidential reasoning. This can be used as a basis for a new way to implement Bayesian Networks that allows for approximations to the value of the posterior probabilities, and also points to a way that Bayesian networks can be extended beyond a propositional language. 1
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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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...
Explaining Incompatibilities in Data Dictionary Design through Abduction
 DATA AND KNOWLEDGE ENGINEERING
, 1994
"... Logic programming with abductive reasoning is used during the realization of a data dictionary with a particular methodology. Some methodological steps are represented by means of a set of rules augmented with integrity constraints which capture the presence of incompatible concepts. Explanations ..."
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Cited by 2 (0 self)
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Logic programming with abductive reasoning is used during the realization of a data dictionary with a particular methodology. Some methodological steps are represented by means of a set of rules augmented with integrity constraints which capture the presence of incompatible concepts. Explanations for incompatibility can be inferred by exploiting abductive reasoning. To this end a new proposal for the computation of hypotheses in an abductive framework where the theory is a general logic program, is presented. It is based on a suitable manipulation of minimal threevalued models of the logic program. A method to compute threevalued minimal models of a general logic program is given.
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"... Representing diagnostic knowledge for probabilistic Horn abduction 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 ..."
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Representing diagnostic knowledge for probabilistic Horn abduction 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
Completing Fault Models for Abductive Diagnosis
, 1996
"... this report we give formal presenta4 tions of the parametrization and instantiation methods. To do so requires formalizing some common assumptions about the form of system descriptions. General principles for organizing system descriptions have been discussed by [Genesereth 1984]. We make explicit t ..."
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this report we give formal presenta4 tions of the parametrization and instantiation methods. To do so requires formalizing some common assumptions about the form of system descriptions. General principles for organizing system descriptions have been discussed by [Genesereth 1984]. We make explicit the relationships between the background theory (logic and physics), components and connections between components. The formalization is sufficiently general to accommodate efficient descriptions of device types, behavioral modes and impossible behaviors as well as nondeterministic device behavior and temporal dependencies. Given such system descriptions we show how the parametrization and instantiation methods can be implemented and compare their advantages and disadvantages.
Integrating Induction and Abduction in Logic Programming
"... We propose an approach for the integration of abduction and induction in Logic Programming. We de ne an Abductive Learning Problem as an extended Inductive Logic Programming problem where both the background and target theories are abductive theories and where abductive derivability is used as the c ..."
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We propose an approach for the integration of abduction and induction in Logic Programming. We de ne an Abductive Learning Problem as an extended Inductive Logic Programming problem where both the background and target theories are abductive theories and where abductive derivability is used as the coverage relation instead of deductive derivability. The two main bene ts of this integration are the possibility of learning in presence of incomplete knowledge and the increased expressive power of the background and target theories. We present the system LAP (Learning Abductive Programs) that is able to solve this extended learning problem and we describe, by means of examples, four di erent learning tasks that can be performed by the system: learning from incomplete knowledge, learning rules with exceptions, learning from integrity constraints and learning recursive predicates.