Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
References
All Topics
Computer Science
Artificial Intelligence

Abduction as belief revision

Profile image of Craig BoutilierCraig Boutilier

1995, Artificial intelligence

https://doi.org/10.1016/0004-3702(94)00025-V
visibility

…

description

62 pages

link

1 file

Abstract

We propose a model of abduction based on the revision of the epistemic state of an agent. Explanations must be sufficient to induce belief in the sentence to be explained (for instance, some observation), or ensure its consistency with other beliefs, in a manner that adequately accounts for factual and hypothetical sentences. Our model will generate explanations that nonmonotonically predict an observation, thus generalizing most current accounts, which require some deductive relationship between explanation and observation. It also provides a natural preference ordering on explanations, defined in terms of normality or plausibility. To illustrate the generality of our approach, we reconstruct two of the key paradigms for model-based diagnosis, abductive and consistency-based diagnosis, within our framework. This reconstruction provides an alternative semantics for both and extends these systems to accommodate our predictive explanations and semantic preferences on explanations. It also illustrates how more general information can be incorporated in a principled manner.

Related papers

On Explanatory Belief revision systems: Initiation Grant Proposal
A V Ravishankar Sarma

In this project, a new model for belief revision based on abduction will be elaborated within the framework of AGM belief revision. We construe "abduction" as the process of reasoning in which explanatory (causal) hypothesis are formed and evaluated. Our basic hypothesis is that agent seeks explanation, before she accepts and integate the new evidential information with the old. As a result, it generates heirarchies of explanation and out of which causal ex- planations are considered to be most plaussible explanations. Here, not all explanations are acceptable for abduction, but only the best or atleast good ones (41). The dynamics of belief based on plaussibility of explanations would give us a reasonable mechanism whether or not to give priority to the incomming information. In this proposed research, we will (1) extend AGM framework with abductive revision operators (2) we characterize such revision with an ordering called causal epistemic entrenchment ordering. We prov...

View PDFchevron_right
Belief Revision Controlled by Meta-abduction
John R Josephson

Logic Journal of the IGPL, 2006

We have described the workings of a dynamic abducer, an abductive reasoning agent that continually fuses information from multiple sources to "maintain situation awareness", that is, to maintain a best estimate of the situation based on incoming data. In particular, we have described a special class of problems of entity tracking and re-identification, and described in some detail how a dynamic abducer for this problem is able, under some circumstances, to appropriately "change its mind". and explicitly revise the conclusion of an earlier reasoning step. This ability results from performing "meta-abductive" reasoning in situations where no plausible hypotheses are available for explaining a new observation report. We have also described ASAS-Smart, an implementation of such a dynamic abducer, and illustrated its capabilities by tracing its reasoning on two example cases. Although the present system is in many ways simple, idealized, and domain-specific, we believe that it illustrates principles of reasoning that have broad applicability.

View PDFchevron_right
On the Relationship Between Abduction and Deduction
Daniele Theseider Dupre

Journal of Logic and Computation, 1991

View PDFchevron_right
A Measure of Arbitrariness in Abductive Explanations
Irina Trubitsyna

Theory and Practice of Logic Programming, 2014

We study the framework of abductive logic programming extended with integrity constraints. For this framework, we introduce a new measure of the simplicity of an explanation based on its degree ofarbitrariness: the more arbitrary the explanation, the less appealing it is, with explanations having no arbitrariness — they are calledconstrained— being the preferred ones. In the paper, we study basic properties of constrained explanations. For the case when programs in abductive theories are stratified we establish results providing a detailed picture of the complexity of the problem to decide whether constrained explanations exist.

View PDFchevron_right
Abduction, that ubiquitous form of reasoning
Ramón Pinero

Expert Systems with Applications, 1998

In recent years, attention has been devoted to abduction, a hypothetical form of non-monotonic reasoning that tries to fit the best 'explanation' to a given observation. In this paper we present a collection of applications of automated abductive reasoning developed in the Center for Artificial Intelligence of the ITESM (Monterrey, Mexico) in the last five years, covering a range from natural language understanding to software re-use.

View PDFchevron_right
Under consideration for publication in Theory and Practice of Logic Programming 1 A Measure of Arbitrariness in Abductive Explanations
Ester Zumpano

2016

We study the framework of abductive logic programming extended with integrity constraints. For this framework, we introduce a new measure of the simplicity of an explanation based on its degree of arbitrariness: the more arbitrary the explanation, the less appealing it is, with explanations having no arbitrariness-they are called constrained-being the preferred ones. In the paper, we study basic properties of constrained explanations. For the case when programs in abductive theories are stratified we establish results providing a detailed picture of the complexity of the problem to decide whether constrained explanations exist.

View PDFchevron_right
An epistemic and dynamic approach to abductive reasoning: selecting the best explanation
Angel Nepomuceno

2013

Previous works have discussed how to represent abductive reasoning in a dynamic epistemic logic framework. It has been discussed how to define an abductive problem, how to define an abductive solution and what to do with the best solution, once this one has been selected. The present work discusses two possible ways in which the agent can select 'the best' explanation.

View PDFchevron_right
On the Generation of Alternative Explanations with Implications for Belief Revision
Eugene Santos

In general, the best explanation for a given observation makes no promises on how good it is with respect to other alternative explanations. A major de ciency of message-passing schemes for belief revision in Bayesian networks is their inability to generate alternatives beyond the second best. In this paper, we present a general approach based on linear constraint systems that naturally generates alternative explanations in an orderly and highly e cient manner. This approach is then applied to cost-based abduction problems as well as belief revision in Bayesian networks.

View PDFchevron_right
Integrating Abduction and Inference to the Best Explanation
Michael Shaffer

European Journal of Pragmatism and American Philosophy

View PDFchevron_right
A unified model for abduction-based reasoning
Shengrui Wang

IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 1998

View PDFchevron_right

References (62)

  1. Ernest W. Adams. The Logic of Conditionals. D.Reidel, Dordrecht, 1975.
  2. Carlos Alchourrón, Peter Gärdenfors, and David Makinson. On the logic of theory change: Partial meet contraction and revision functions. Journal of Symbolic Logic, 50:510-530, 1985.
  3. Craig Boutilier. Inaccessible worlds and irrelevance: Preliminary report. In Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, pages 413-418, Sydney, 1991.
  4. Craig Boutilier. Epistemic entrenchment in autoepistemic logic. Fundamenta Informaticae, 17(1-2):5-30, 1992.
  5. Craig Boutilier. The probability of a possibility: Adding uncertainty to default rules. In Proceedings of the Ninth Conference on Uncertainty in AI, pages 461-468, Washington, D.C., 1993.
  6. Craig Boutilier. Revision sequences and nested conditionals. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pages 519-525, Chambery, FR, 1993.
  7. Craig Boutilier. Conditional logics of normality: A modal approach. Artificial Intelligence, 1994. (in press).
  8. Craig Boutilier. Toward a logic for qualitative decision theory. In Proceedings of the Fourth International Conference on Principles of Knowledge Representation and Reasoning, Bonn, 1994. To appear.
  9. Craig Boutilier. Unifying default reasoning and belief revision in a modal framework. Artificial Intelligence, 1994. (in press).
  10. Craig Boutilier and Veronica Becher. Abduction as belief revision. Technical Report 93-23, University of British Columbia, Vancouver, 1993.
  11. Craig Boutilier and Moisés Goldszmidt. Revision by conditional beliefs. In Proceedings of the Eleventh National Conference on Artificial Intelligence, pages 649-654, Washington, D.C., 1993.
  12. Gerhard Brewka. Preferred subtheories: An extended logical framework for default reasoning. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, pages 1043-1048, Detroit, 1989.
  13. Luca Console and Pietro Torasso. A spectrum of logical definitions of model-based diagnosis. Computational Intelligence, 7:133-141, 1991.
  14. Randall Davis. Diagnostic reasoning based on structure and behavior. Artificial Intelligence, 24:347-410, 1984.
  15. Johan de Kleer. Focusing on probable diagnoses. In Proceedings of the Ninth National Confer- ence on Artificial Intelligence, pages 842-848, Anaheim, 1991.
  16. Johan de Kleer, Alan K. Mackworth, and Raymond Reiter. Characterizing diagnoses. In Proceedings of the Eighth National Conference on Artificial Intelligence, pages 324-330, Boston, 1990.
  17. Johan de Kleer and Brian C. Williams. Diagnosing multiple faults. Artificial Intelligence, 32:97-130, 1987.
  18. James P. Delgrande. An approach to default reasoning based on a first-order conditional logic: Revised report. Artificial Intelligence, 36:63-90, 1988.
  19. Gerhard Friedrich and Wolfgang Nejdl. Choosing observations and actions in model-based diagnosis/repair systems. In Proceedings of the Third International Conference on Principles of Knowledge Representation and Reasoning, pages 489-498, Cambridge, 1992.
  20. Antony Galton. A critique of Yoav Shoham's theory of causal reasoning. In Proceedings of the Ninth National Conference on Artificial Intelligence, pages 355-359, Anaheim, 1991.
  21. Peter Gärdenfors. On the logic of relevance. Synthese, 37(3):351-367, 1978.
  22. Peter Gärdenfors. Knowledge in Flux: Modeling the Dynamics of Epistemic States. MIT Press, Cambridge, 1988.
  23. Peter Gärdenfors and David Makinson. Nonmonotonic inference based on expectations. Artificial Intelligence, 65:197-245, 1994.
  24. M. R. Genesereth. The use of design descriptions in automated diagnosis. Artificial Intelligence, 24:411-436, 1984.
  25. Matthew L. Ginsberg. Counterfactuals. Artificial Intelligence, 30(1):35-79, 1986.
  26. Moisés Goldszmidt and Judea Pearl. On the consistency of defeasible databases. Artificial Intelligence, 52:121-149, 1991.
  27. Moisés Goldszmidt and Judea Pearl. Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions. In Proceedings of the Third Interna- tional Conference on Principles of Knowledge Representation and Reasoning, pages 661-672, Cambridge, 1992.
  28. Adam Grove. Two modellings for theory change. Journal of Philosophical Logic, 17:157-170, 1988.
  29. Carl G. Hempel. Philosophy of Natural Science. Prentice-Hall, Englewood Cliffs, NJ, 1966.
  30. Jerry R. Hobbs, Mark Stickel, Douglas Appelt, and Paul Martin. Interpretation as abduction. Technical Note 499, SRI International, Menlo Park, December 1990.
  31. Kurt Konolige. Abduction versus closure in causal theories. Artificial Intelligence, 53:255-272, 1992.
  32. Kurt Konolige. Using default and causal reasoning in diagnosis. In Proceedings of the Third International Conference on Principles of Knowledge Representation and Reasoning, pages 509-520, Cambridge, 1992.
  33. Sarit Kraus, Daniel Lehmann, and Menachem Magidor. Nonmonotonic reasoning, preferential models and cumulative logics. Artificial Intelligence, 44:167-207, 1990.
  34. Daniel Lehmann. What does a conditional knowledge base entail? In Proceedings of the First International Conference on Principles of Knowledge Representation and Reasoning, pages 212-222, Toronto, 1989.
  35. Hector J. Levesque. A knowledge level account of abduction. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, pages 1061-1067, Detroit, 1989.
  36. Hector J. Levesque. All I know: A study in autoepistemic logic. Artificial Intelligence, 42:263- 309, 1990.
  37. David Lewis. Causation. Journal of Philosophy, 70:556-567, 1973.
  38. John McCarthy. Epistemological problems in artificial intelligence. In Ronald J. Brachman and Hector J. Levesque, editors, Readings in Knowledge Representation, pages 24-30. Morgan- Kaufmann, Los Altos, 1977. 1985.
  39. Sheila McIlraith and Ray Reiter. On experiments for hypothetical reasoning. In Proc. 2nd International Workshop on Principles of Diagnosis, pages 1-10, Milan, October 1991.
  40. Donald Nute. Topics in Conditional Logic. D.Reidel, Dordrecht, 1980.
  41. Judea Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, 1988.
  42. Judea Pearl. A calculus of pragmatic obligation. In Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, pages 12-20, Washington, D.C., 1993.
  43. David Poole. A logical framework for default reasoning. Artificial Intelligence, 36:27-47, 1988.
  44. David Poole. Explanation and prediction: An architecture for default and abductive reasoning. Computational Intelligence, 5:97-110, 1989.
  45. David Poole. Normality and faults in logic-based diagnosis. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, pages 1304-1310, Detroit, 1989.
  46. David Poole. Representing diagnostic knowledge for probabilistic horn abduction. In Proceed- ings of the Twelfth International Joint Conference on Artificial Intelligence, pages 1129-1135, Sydney, 1991.
  47. Karl R. Popper. The Logic of Scientific Discovery. Basic Books, New York, 1959.
  48. W.V. Quine and J.S. Ullian. The Web of Belief. Random House, New York, 1970.
  49. Raymond Reiter. A theory of diagnosis from first principles. Artificial Intelligence, 32:57-95, 1987.
  50. Raymond Reiter and Johan de Kleer. Foundations of assumption-based truth maintenance systems: Preliminary report. In Proceedings of the Sixth National Conference on Artificial Intelligence, pages 183-188, Seattle, 1987.
  51. Raymond Reiter and Alan K. Mackworth. A logical framework for depiction and image inter- pretation. Artificial Intelligence, 41:125-155, 1989.
  52. Nicholas Rescher. Peirce's Philosophy of Science: Critical Studies in his Theory of Induction and Scientific Method. University of Notre Dame Press, Notre Dame, 1978.
  53. Krister Segerberg. Modal logics with linear alternative relations. Theoria, 36:310-322, 1970.
  54. Murray Shanahan. Prediction is deduction but explanation is abduction. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, pages 1140-1145, Detroit, 1989.
  55. Yoav Shoham. Nonmonotonic reasoning and causation. Cognitive Science, 14:213-252, 1990.
  56. Herbert A. Simon. Nonmonotonic reasoning and causation: Comment. Cognitive Science, 15:293-300, 1991.
  57. Robert C. Stalnaker. A theory of conditionals. In W.L. Harper, R. Stalnaker, and G. Pearce, editors, Ifs, pages 41-55. D. Reidel, Dordrecht, 1968. 1981.
  58. Bas C. van Fraassen. The pragmatics of explanation. American Philosophical Quarterly, 14:143-150, 1977.
  59. A Proofs of Main Theorems Proposition 3.1 If ;
  60. K, then 2 (K ) iff : 2 K : .
  61. Proof Let M be an appropriate K-revision model for the contraction and revision function in question. We have 2 (K ) iff is true at each -world in k(K )k, i.e., iff holds at (kKk min(:
  62. \ k k (since 2 K). This holds iff there is no -world in min(: ) iff : 2 K : . Proposition 3.2 If : ; : 2 K, then : 2 (K : ) : iff 2 K . Proof The proof is similar to that of Proposition 3.1. Proposition 3.3 If ; 2 K then (predictively) explains iff : ) : . Proof If ; 2 K then condition (A), ) , holds trivially (since kKk = min( ) = min( )). Proposition 3.4 If ; ; : ; : 6 2 K then (predictively) explains iff ) iff : ) : . Proof If ; ; : ; : 6 2 K, then min( ) kKk and min(: ) kKk. Thus, min( ) k k iff min(: ) k: k. Proposition 3.5 If : ; : 2 K then (predictively) explains iff ) .

Related papers

Abduction As Belief Revision: A Model of Preferred Explanations
Verónica Becher

1993

We propose a natural model of abduction based on the revision of the epistemic state of an agent. We require that explanations be sufficient to induce belief in an observation in a manner that adequately accounts for factual and hypothetical observations. Our model will generate explanations that nonmonotonically predict an observation, thus generalizing most current accounts, which require some deductive relationship between explanation and observation. It also provides a natural preference ordering on explanations, defined in terms of normality or plausibility. We reconstruct the Theorist system in our framework, and show how it can be extended to accommodate our predictive explanations and semantic preferences on explanations.

View PDFchevron_right
To appear, Artificial Intelligence, 1995 Abduction as Belief Revision
Verónica Becher

2010

We propose a model of abduction based on the revision of the epistemic state of an agent. Explanations must be sufficient to induce belief in the sentence to be explained (for instance, some observation), or ensure its consistency with other beliefs, in a manner that adequately accounts for factual and hypothetical sentences. Our model will generate explanations that nonmonotonically predict an observation, thus generalizing most current accounts, which require some deductive relationship between explanation and observation. It also provides a natural preference ordering on explanations, defined in terms of normality or plausibility. To illustrate the generality of our approach, we reconstruct two of the key paradigms for model-based diagnosis, abductive and consistency-based diagnosis, within our framework. This reconstruction provides an alternative semantics for both and extends these systems to accommodate our predictive explanations and semantic preferences on explanations. It ...

View PDFchevron_right
Abductive Logics in a Belief Revision Framework
Hervé ZWIRN

Journal of Logic, Language and Information, 2005

Abduction was first introduced in the epistemological context of scientific discovery. It was more recently analyzed in artificial intelligence, especially with respect to diagnosis analysis or ordinary reasoning. These two fields share a common view of abduction as a general process of hypotheses formation. More precisely, abduction is conceived as a kind of reverse explanation where a hypothesis H can be abduced from events E if H is a “good explanation” of E. The paper surveys four known schemes for abduction that can be used in both fields. Its first contribution is a taxonomy of these schemes according to a common semantic framework based on belief revision. Its second contribution is to produce, for each non-trivial scheme, a representation theorem linking its semantic framework to a set of postulates. Its third contribution is to present semantic and axiomatic arguments in favor of one of these schemes, “ordered abduction,” which has never been vindicated in the literature.

View PDFchevron_right
Explanations, belief revision and defeasible reasoning
Guillermo R Simari

Artificial Intelligence, 2002

We present different constructions for non-prioritized belief revision, that is, belief changes in which the input sentences are not always accepted. First, we present the concept of explanation in a deductive way. Second, we define multiple revision operators with respect to sets of sentences (representing explanations), giving representation theorems. Finally, we relate the formulated operators with argumentative systems and default reasoning frameworks.

View PDFchevron_right
Abduction to plausible causes: An event-based model of belief update
Craig Boutilier

1996

The Katsuno and Mendelzon (KM) theory of belief update has been proposed as a reasonable model for revising beliefs about a changing world. However, the semantics of update relies on information which is not readily available. We describe an alternative semantical view of update in which observations are incorporated into a belief set by:(a) explaining the observation in terms of a set of plausible events that might have caused that observation; and (b) predicting further consequences of those explanations.

View PDFchevron_right

Related topics

  • Cognitive Science
  • Artificial Intelligence
  • AI Planning (Artificial Intellig...
  • Belief Revision
  • Model-based diagnosis
  • Current Account Deficit

    • Cited by

    The Unexpected Aspects of Surprise
    Emiliano Lorini

    International Journal of Pattern Recognition and Artificial Intelligence, 2006

    Some symbolic AI models for example BDI (belief, desire, intention) models are conceived as explicit and operational models of the intentional pursuit and belief dynamics. The main concern of these models is to provide a clear understanding of the functional roles of different kinds of epistemic and motivational states (beliefs, acceptances, expectations, intentions, goals, desires etc...), of the relational properties among them. Mental configurations of appraisal (involving different kinds of motivational and epistemic states) which correspond to particular cognitive emotions such as disappointment, fear, relief, shame etc... have been analyzed by several authors close to the BDI theoretical tradition. The main objective of this work is a formal analysis of Surprise in a BDI-like cognitive architecture. A clarification of the functional role of Surprise in a BDI-like cognitive architecture with respect to resource bounded belief revision is given.

    View PDFchevron_right
    A Computational Framework for Procedural Abduction Done by Smart Cyber-Physical Systems
    Imre Horvath

    Designs

    To be able to provide appropriate services in social and human application contexts, smart cyber-physical systems (S-CPSs) need ampliative reasoning and decision-making (ARDM) mechanisms. As one option, procedural abduction (PA) is suggested for self-managing S-CPSs. PA is a knowledge-based computation and learning mechanism. The objective of this article is to provide a comprehensive description of the computational framework proposed for PA. Towards this end, first the essence of smart cyber-physical systems is discussed. Then, the main recent research results related to computational abduction and ampliative reasoning are discussed. PA facilitates beliefs-driven contemplation of the momentary performance of S-CPSs, including a ‘best option’-based setting of the servicing objective and realization of any demanded adaptation. The computational framework of PA includes eight clusters of computational activities: (i) run-time extraction of signals and data by sensing, (ii) recognitio...

    View PDFchevron_right
    The Many Faces of Defeasibility in Defeasible Deontic Logic
    Yao-hua Tan

    Defeasible Deontic Logic, 1997

    View PDFchevron_right
    Entitlement and Evidence
    Martin Smith

    Australasian Journal of Philosophy, 2013

    Entitlement is conceived as a kind of positive epistemic status, attaching to certain propositions, that involves no cognitive or intellectual accomplishment on the part of the beneficiary-a kind of positive epistemic status that is in place by default. In this paper I will argue that the notion of entitlement-or something very like it at any rate-falls out of an idea that may at first blush seem rather disparate; that the evidential support relation can be understood as a kind of variably strict conditional (in the sense of Lewis [1973]). Lewis provided a general recipe for deriving what he termed inner modalities from any variably strict conditional governed by a logic meeting certain constraints. On my proposal, entitlement need be nothing more exotic than the inner necessity associated with evidential support. Understanding entitlement in this way helps to answer some common concerns-in particular, the concern that entitlement could only be a pragmatic, and not genuinely epistemic, status.

    View PDFchevron_right
    A logic for best explanations
    Christian Straßer

    Journal of Applied Non-Classical Logics, 2019

    There are two obstacles confronting efforts to formalize qualitative accounts of inference to the best explanation (IBE). The first is that extant accounts of IBE in philosophy of science remain painfully imprecise and often rely on contextual criteria to evaluate explanations. The second is that our best explanations exhibit unusual logical properties, such as contradiction-intolerance and irreflexivity. This paper aims to surmount these challenges by utilizing a new, more precise theory that treats explanations as expressions that codify defeasible inferences. To formalize this account, we provide a sequent calculus in which IBE serves as an elimination rule for a connective that exhibits many of the properties associated with the behavior of the English expression "That.. . best explains why.. . ". We first construct a calculus that encodes these properties at the level of the turnstile, i.e. as a metalinguistic expression for classes of defeasible consequence relations. We then show how this calculus can be conservatively extended over a language that contains a best-explains-why operator.

    View PDFchevron_right
    580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved