Abstract
Abductive reasoning has gained increasing interest in many fields of AI research. Its utility was first observed for diagnostic tasks (cf. [Pople, 19731 or, e.g., [Console and Torasso, 1991; Console et al., 1991b]), but as many researchers have shown it is not limited to this use. Currently under investigation or suggested are such different applications as plan recognition (e.g., [Dragoni and Puliti, 1994; Helft and Konolige, 1990; Bauer and Paul, 1993; Bauer et al., 1993]), text understanding and generation (e.g., [Stickel, 1990]), program debugging (cf. [Charniak and McDermott, 1985]), test generation (see [Mcllraith, 1994]), planning (e.g., [Eshghi, 1991; Stone, 1998]), user modeling (cf. [Poole, 1988]), database updates (e.g., [Kakas and Mancarella, 1990a}), case-based reasoning (cf. [Leake, 1993; Satoh, 1998]), learning (cf. [Kakas et al., 1998; Lamma et al., 19971 or [Thompson and Mooney, 1990, temporal reasoning (e.g., [Li and Pereira, 19961), constraint handling (e.g., [Bürckert and Nutt, 1992; Wetzel and Toni, 1998]) or vision (cf. [Charniak and McDermott, 1985]).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
H. Adé and M. Denecker. RUTH: An ILP theory revision system. In Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems, 1994.
D. Allemang, M. Tanner, T. Bylander, and J. Josephson. Computational complexity of hypothesis assembly. In Proceedings of the 10th International Joint Conference on Artificial Intelligence,pages 1112–1117, 1987.
D.E. Appelt and M. Pollack. Weighted abduction for plan ascription. Technical report, Artificial Intelligence Center and Center for the Study of Language and Information, SRI International, Menlo Park, California, 1990.
D.E. Appelt. A theory of abduction based on model preference. In Proceedings of the AAAI90 Spring Symposium on Abduction, 1990.
M. Bauer and G. Paul. PHI —a logic-based tool for intelligent help systems. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, pages 460–466, 1993.
M. Bauer, S. Biundo, D. Dengler, J. Koehler, and G. Paul. Logic-based plan recognition for intelligent help systems. In Current Trends in AI Planning, Proceedings of the 2nd European Workshop on Planning,pages 60–73, 1993.
C. Boutilier and V. Becher. Abduction as belief revision. Artificial Intelligence, 77: 43–94, 1995.
C. Boutilier. Abduction to plausible causes: an event-based model of belief update. Artificial Intelligence, 83: 143–166, 1996.
G. Brewka and K. Konolige. An abductive framework for general logic programs and other non-monotonic systems. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, pages 9–15, 1993.
G. Brewka. Preferred subtheories: an extended logical framework for default reasoning. In Proceedings of the 12th International Joint Conference on Artificial Intelligence, pages 1043–1048, 1991.
V. Brusoni, L. Console, P. Terenziani, and D.T. Dupré. An efficient algorithm for temporal abduction. In Proceedings of the 5th Congress of the Italian Association for Artificial Intelligence,pages 195–206, 1997.
H.J. Bürckert and W. Nutt. On abduction and answer generation through constrained resolution. Technical report, German Reseach Center for AI (DFKI GmbH ), 1992.
T. Bylander, D. Allemang, M. Tanner, and J. Josephson. The computational complexity of abduction. Artificial Intelligence,49:25–60, 1991.
E. Chamiak and P. McDermott. Introduction to Artificial Intelligence. Addison Wesley, Menlo Park, California, 1985.
E. Charniak and S.E. Shimony. Probabilistic semantics for cost based abduction. In Proceedings of the 8th National Conference on Artificial Intelligence, pages 106–111, 1990.
E. Chamiak and S.E. Shimony. Cost-based abduction and MAP explanation. Artificial Intelligence, 66 (2): 345–374, 1994.
L. Console and P. Torasso. A spectrum of logical definitions of model-based diagnosis. Computational Intelligence, 7: 133–141, 1991.
L. Console, D.T. Dupré, and P. Torasso. On the relationship between abduction and deduction. Journal of Logic and Computation, 1(5):661–690,1991.
L. Console, L. Portinale, and D.T. Dupré. Focussing abductive diagnosis. Al Communications,4(2/3):88–97, 1991.
P. Cox and T. Pietrzykowski. A complete nonredundant algorithm for reversed skolemization. Theoretical Computer Science, 28: 239–261, 1984.
P. Cox and T. Pietrzykowski. Causes for events: Their computation and application. In Proceedings CADE 86, pages 608–621, 1986.
J. de Kleer, A.K. Mackworth, and R. Reiter. Characterizing diagnoses. In Proceedings of the 8th National Conference on Artificial Intelligence,pages 324–330, 1990.
R. Dechter, I. Meiri, and J. Pearl. Temporal constraint networks. Artificial Intelligence,49:61–95, 1991.
M. Denecker and D. de Schreye. SLDNFA: An abductive procedure for normal logic programs. In ICSLP, pages 868–700, 1992.
A.F. Dragoni and P. Puliti. Mental states recognition from speech acts through abduction. In Proceedings of the 11th European Conference on Artificial Intelligence, pages 183–187, 1994.
P.M. Dung. Negation as hypothesis: an abductive foundation for logic programming. In Proceedings of the 8th International Conference on Logic Programming, pages 3–17, 1991.
P.M. Dung. On the acceptability of arguments and its fundamental role in nonmonotonic reasoning and logic programming. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, pages 852–859, 1993.
D.T. Dupré and M. Rossotto. The different roles of abstraction in abductive reasoning. In Proceedings of the Italian Association for Artificial Intelligence on Topics in Artificial Intelligence, volume 992 of LNAI, pages 211–216, 1995.
T. Eiter and G. Gottlob. The complexity of logic-based abduction. Journal of the Association for Computing Machinery, 42 (1): 3–42, 1995.
K. Eshghi and R. Kowalski Abduction through deduction. Technical report, Imperial College of Science and Technology, Department of Computing, 1988.
K. Eshghi and R. Kowalski. Abduction compared with negation by failure. In Proceedings of the 6th International Conference on Logic Programming, 1989.
K. Eshghi. Abductive planning with Event Calculus. In Logic Programming: Proc. of the Fifth International Conference and Symposium (Volume 1), pages 562–579, 1991.
K. Eshghi. A tractable class of abduction problems. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, pages 3–8, 1993.
T.H. Fung and R.A. Kowalski. The IFF proof procedure for abductive logic programming. The Journal of Logic Programming, 1997.
D. M. Gabbay. Abduction in labelled deductive systems. This volume, pp. 101–156, 2000.
Th.A. Goudge. The Thought of C.S. Peirce. Dover Publications Inc., New York, 1950.
N. Helft and K. Konolige. Plan recognition as abduction and relevance. Draft version, Artificial Intelligence Center, SRI International, Menlo Park, California, 1990.
J. R. Hobbs, M. Stickel, P. Martin, and D. Edwards. Interpretation as abduction. Draft version, SRI International, Artificial Intelligence Center, Menlo Park, CA, 1989.
K. Inoue and C. Sakama. Abductive framework for nonmonotonic theory change. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, pages 204–210, 1995.
K. Inoue. An abductive procedure for the CMS/ATMS. In Proceedings of the Workshop on Truth Maintenance Systems (ECAI-90), volume 515 of LNAI, pages 34–53, 1990.
C.G. Jung. Situated abstraction planning by abductive temporal reasoning. In Proceedings of the 13th European Conference on Artificial Intelligence, pages 383–387, 1998.
A.C. Kakas and P. Mancarella. Database updates through abduction. In Proceedings of the 16th Conference on Very Large Databases, 1990.
A.C. Kakas and P. Mancarella. Generalized stable models: a semantics for abduction. In Proceedings of the 9th European Conference on Artificial Intelligence, pages 385–391, 1990.
A.C. Kakas and F. Riguzzi. Learning with abduction. In Proceedings of the 7th International Workshop on Inductive Logic Programming, volume 1297 of LNAI, pages 181–188, 1997.
A.C. Kakas, R.A. Kowalski, and E Toni. The role of abduction in logic programming. In D.M. Gabbay, C.J. Hogger, and J.A. Robinson, editors, Handbook of Logic in Artificial Intelligence and Logic Programming,pages 235–324. Oxford University Press, 1998.
K. Konolige. Closure + minimization implies abduction. In PRICAI-90, Nagoya, Japan, 1990.
R. Kowalski and M. Sergot. A logic based calculus of events. New Generation Computing, 4: 67–95, 1986.
E. Lamma, P. Mello, M. Milano, and F. Riguzzi. Introducing abduction into (extensional) inductive logic programming systems. In Proceedings of the 5th Congress of the Italian Association for Artificial Intelligence: Advances in Artificial Intelligence (AI*IA-97),volume 1321 of LNAI,pages 183–194, 1997.
D.B. Leake. Focusing construction and selection of abductive hypotheses. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, pages 24–29, 1993.
H. Levesque. A knowledge-level account of abduction. In Proceedings of the 11 th International Joint Conference on Artificial Intelligence, pages 1061–1067, 1989.
R. Li and L.M. Pereira. Temporal reasoning with abductive logic programming. In Proceedings of the 12th European Conference on Artificial Intelligence, pages 13–17, 1996.
M. Cialdea Mayer and F. Pirri. A study on the logic of abduction. In Proceedings of the 12th European Conference on Artificial Intelligence, pages 18–22, 1996.
S. Mcllraith. Generating tests using abduction. In Proceedings of the 4th International Conference on Principles of Knowledge Representation and Reasoning, pages 449–460, 1994.
Ch.G. Morgan. Hypothesis generation by machine. Artificial Intelligence, 2: 179–187, 1971.
H.T. Ng and R.J. Mooney. On the role of coherence in abductive explanation. In Proceedings of the 8th National Conference on Artificial Intelligence, pages 337–342, 1990.
H.T. Ng and R.J. Mooney. Abductive plan recognition and diagnosis: a comprehensive empirical evaluation. In Proceedings of the 3rd International Conference on Principles of Knowledge Representation and Reasoning, pages 499–508, 1992.
P. O’Rorke. Focusing on most important explanations: decision-theoretic Horn abduction. In Proceedings of the 12th National Conference on Artificial Intelligence, pages 275–280, 1994.
G. Paul. Approaches to abductive reasoning—an overview. Artificial Intelligence Review, 7: 109–152, 1993.
J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, 1988.
C.S. Peirce. Collected Papers of Charles Sanders Peirce (eds. C. Hartshorne et al.). Harvard University Press, 1931–1958.
D. Poole. A logical framework for default reasoning. Artificial Intelligence, 36: 27–47, 1988.
D. Poole. Explanation and prediction: an architecture for default and abductive reasoning. Computational Intelligence, 5: 97–110, 1989.
D. Poole. Normality and faults in logic-based diagnosis. In Proceedings of the 11 th International Joint Conference on Artificial Intelligence, pages 1304–1310, 1989.
D. Poole. Representing diagnostic knowledge for probabilistic Horn abduction. In Proceedings of the 12th International Joint Conference on Artificial Intelligence, pages 1129–1135, 1991.
D. Poole. Probabilistic Horn abduction and Bayesian networks. Artificial Intelligence, 64: 81–129, 1993.
H.E. Pople, Jr. On the mechanization of abductive logic. In Proceedings of the 3rd International Joint Conference on Artificial Intelligence, pages 147–151, 1973.
J. Reggia. Diagnostic expert systems based on a set covering model. International Journal of Man-Machine Studies, November 83: 437–460, 1988.
R. Reiter. A logic for default reasoning. Artificial Intelligence, 13(2):81–132,1980.
R. Reiter. A theory of diagnosis from first principles. Artificial Intelligence, 32: 57–95, 1987.
C. Ribeiro and A. Porto. Abduction in temporal reasoning. In Proceedings of the 1st International Conference on Temporal Logic, pages 349–364, 1994.
E. Santos, Jr. A linear constraint satisfaction approach to cost-based abduction. Artificial Intelligence, 65 (1): 1–28, 1994.
K. Satoh and N. Iwayama. Computing abduction by using the TMS. In Proceedings of the 8th International Conference on Logic Programming, pages 505–520, 1991.
K. Satoh and N. Iwayama. A correct top-down proof procedure for general logic programs with integrity constraints. In Proceedings of the 3rd International Workshop on Extensions of Logic Programming, pages 19–34, 1992.
K. Satoh. Using two level abduction to decide similarity of cases. In Proceedings of the 13th European Conference on Artificial Intelligence, pages 398–402, 1998.
B. Selman and H.L. Levesque. Abductive and default reasoning: A computational core. In Proceedings of the 8th National Conference on Artificial Intelligence, pages 343–348, 1989.
B. Selman and H.J. Levesque. Support set selection for abductive and default reasoning. Artificial Intelligence, 82: 259–272, 1996.
M. Shanahan. Prediction is deduction but explanation is abduction. In Proceedings of the 11th International Joint Conference on Artificial Intelligence,pages 1055–1060, 1989.
J.W. Smith. RED: A Classificatory and Abductive Expert System. PhD thesis, Ohio State University, Laboratory for Artificial Intelligence Research, Department for Computer Science, 1985.
M. Stickel. Rationale and methods for abductive reasoning in natural-language interpretation. In R. Studer, editor, Natural Language and Logic, pages 331–352. Springer-Verlag, Berlin, Heidelberg, New York, 1990.
M. Stone. Abductive planning with sensing. In Proceedings of the 15th National Conference on Artificial Intelligence, pages 631–636, 1998.
C.A. Thompson and R.J. Mooney. Inductive learning for abductive diagnosis. In Proceedings of the 12th National Conference on Artificial Intelligence, pages 664–669, 1994.
F. Toni and R.A. Kowalski. Reduction of abductive logic programs to normal logic programs. In Proceedings of the 12th International Conference on Logic Programming, pages 367–382, 1995.
A. Wæm. Reactive abduction. In Proceedings of the 10th European Conference on Artificial Intelligence, pages 159–163, 1992.
G. Wetzel and F. Toni. Semantic query optimization through abduction and constraint handling. In Proceedings of the 3rd International Conference on Flexible Query Answering Systems (FQAS-98), volume 1495 of LNAI, pages 366–381, 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Paul, G. (2000). AI Approaches to Abduction. In: Gabbay, D.M., Kruse, R. (eds) Abductive Reasoning and Learning. Handbook of Defeasible Reasoning and Uncertainty Management Systems, vol 4. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-1733-5_2
Download citation
DOI: https://doi.org/10.1007/978-94-017-1733-5_2
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-5560-6
Online ISBN: 978-94-017-1733-5
eBook Packages: Springer Book Archive