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A Model for Belief Revision
, 1988
"... It is generally recognized that the possibdity of detecting contradictions and identifying their sources is an important feature of an intelligent system. Systems that are able to detect contradictions, identify their causes, or readjust their knowledge bases to remove the contradiction, called Beli ..."
Abstract

Cited by 118 (29 self)
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It is generally recognized that the possibdity of detecting contradictions and identifying their sources is an important feature of an intelligent system. Systems that are able to detect contradictions, identify their causes, or readjust their knowledge bases to remove the contradiction, called Belief Revision Systems. Truth Maintenance Systems, or Reason Maintenance Systems. have been studied by several researchers in Artificial bttelligence ( AI). In this paper, we present a logic suitable for supporting belief revision systems, discuss the properties that a belief revision system based on this logic will exhibit, and present a particular intplementation of our model of a belief revision system. The system we present differs from most of the systems developed so far in three respects: First, it is baseti on a logic that was developed to support belief revision systems. Second, it uses the rules of inference of the logic to automatically compute the dependencies among propositions rather than having to force the user to do titis, as in many existing systems. Third, it was the first belief revision system whose implementation relies on the manipulation of sets of assumptions, not justifications.
Perspectives on the Theory and Practice of Belief Functions
 International Journal of Approximate Reasoning
, 1990
"... The theory of belief functions provides one way to use mathematical probability in subjective judgment. It is a generalization of the Bayesian theory of subjective probability. When we use the Bayesian theory to quantify judgments about a question, we must assign probabilities to the possible answer ..."
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Cited by 86 (7 self)
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The theory of belief functions provides one way to use mathematical probability in subjective judgment. It is a generalization of the Bayesian theory of subjective probability. When we use the Bayesian theory to quantify judgments about a question, we must assign probabilities to the possible answers to that question. The theory of belief functions is more flexible; it allows us to derive degrees of belief for a question from probabilities for a related question. These degrees of belief may or may not have the mathematical properties of probabilities; how much they differ from probabilities will depend on how closely the two questions are related. Examples of what we would now call belieffunction reasoning can be found in the late seventeenth and early eighteenth centuries, well before Bayesian ideas were developed. In 1689, George Hooper gave rules for combining testimony that can be recognized as special cases of Dempster's rule for combining belief functions (Shafer 1986a). Similar rules were formulated by Jakob Bernoulli in his Ars Conjectandi, published posthumously in 1713, and by JohannHeinrich Lambert in his Neues Organon, published in 1764 (Shafer 1978). Examples of belieffunction reasoning can also be found in more recent work, by authors
An Overview of Nonmonotonic Reasoning and Logic Programming
 Journal of Logic Programming, Special Issue
, 1993
"... The focus of this paper is nonmonotonic reasoning as it relates to logic programming. I discuss the prehistory of nonmonotonic reasoning starting from approximately 1958. I then review the research that has been accomplished in the areas of circumscription, default theory, modal theories and logic ..."
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Cited by 27 (2 self)
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The focus of this paper is nonmonotonic reasoning as it relates to logic programming. I discuss the prehistory of nonmonotonic reasoning starting from approximately 1958. I then review the research that has been accomplished in the areas of circumscription, default theory, modal theories and logic programming. The overview includes the major results developed including complexity results that are known about the various theories. I then provide a summary which includes an assessment of the field and what must be done to further research in nonmonotonic reasoning and logic programming. 1 Introduction Classical logic has played a major role in computer science. It has been an important tool both for the development of architecture and of software. Logicians have contended that reasoning, as performed by humans, is also amenable to analysis using classical logic. However, workers in the field of artificial 1 This paper is an updated version of an invited Banquet Address, First Interna...
Toward Efficient Default Reasoning (Extended Abstract)
, 1992
"... ) David W. Etherington James M. Crawford AI Principles Research Department 600 Mountain Ave., P.O. Box 636 Murray Hill, NJ 079740636 fether, jcg@research.att.com Abstract Early work on default reasoning was motivated by the need to formalize the notion of "jumping to conclusions". Unfortunate ..."
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) David W. Etherington James M. Crawford AI Principles Research Department 600 Mountain Ave., P.O. Box 636 Murray Hill, NJ 079740636 fether, jcg@research.att.com Abstract Early work on default reasoning was motivated by the need to formalize the notion of "jumping to conclusions". Unfortunately, most existing theories of default reasoning require explicitly considering every possible exceptional case before applying a default rule. They are thus inherently undecidable in the firstorder case, and remain intractable in all but the most restrictive cases. One possible approach to tractable default reasoning is to restrict consistency checks (which engender much of the intractability of default reasoning) to a restricted context. Unfortunately, consistency checking is undecidable in the firstorder case, so a small context in no way guarantees tractability. Another idea is to use linear or polynomial time, but incomplete, consistency checks. Unfortunately, the known tractable check...
Nurture, Nature, Structure a Computational Approach to Learning
"... Contents Summary v Acknowledgements vii 1 Introduction 1 1.1 How do we Learn? . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Justification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Outl ..."
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Contents Summary v Acknowledgements vii 1 Introduction 1 1.1 How do we Learn? . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Justification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 On Concepts and Representation 7 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Different Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.3 Nature of Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.4 Acquisition of Concepts . . . . . . . . . . . . . . . . . . . . . . . 10 2.5 Concepts in Practice . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.6 Criteria of Concept Assessment . . . . . . . . . . . . . . . . . . . 13 2.7 Finding Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.8 Artificial Concep