Abstract
Commonsense knowledge often omits the temporal incidence of facts, and even the ordering between occurrences is only available for some of their instances. Reasoning about the temporal extent of facts and their sequencing becomes complex due to this inherent partiality. The generation of hypotheses is adopted here as a natural way to overcome the difficulties in computing answers to temporal queries. The proposed abductive system performs temporal reasoning in a logic programming framework. Queries are taken as goals and the inference system combines deduction with abduction and constraint solving. The convenience of constraints for dealing with temporal information is widely recognized, their interest being twofold: the representation of essential properties of time and the provision for partial information, allowing flexible bounds on times instead of constant bindings. Inference manipulates a language associating propositions with time periods which are maximal intervals for the proposition. The abductive inference procedure is described here, identifying the constraint operations required. It is also shown that the outcome of a derivation is always consistent with the information in the knowledge base.
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References
James Allen. Maintaining Knowledge About Temporal Intervals. Communications of the ACM, 26(11):832–843, 1983.
James Allen. Towards a General Theory of Action and Time. Artificial Intelligence, (23):123–154, 1984.
R. Dechter, I. Meiri, and J. Pearl. Temporal constraint networks. Artificial Intelligence, 49(1), 1991.
Steve Hanks and Drew McDermott. Default reasoning, nonmonotonic logics and the frame problem. In Proceedings of the 5th National Conference on Artificial Intelligence, pages 328–333. AAAI, 1986.
A. C. Kakas, R. A. Kowalski, and F. Toni. Abductive logic programming. Journal of Logic and Computation, 2(6):719–770, 1992.
H. Kautz and P. Ladkin. Integrating metric and qualitative temporal reasoning. In Proceedings of AAAI'91, 1991.
Robert Kowalski and Marek Sergot. A logic-based calculus of events. New Generation Computing, 4(1):67–95, 1986.
Drew McDermott. A Temporal Logic for Reasoning About Processes and Plans. Cognitive Science, (6):101–155, 1982.
I. Meiri. Combining qualitative and quantitative constraints in temporal reasoning. In Proceedings of AAAI'91, 1991.
Cristina Ribeiro. Representation and Inference of Temporal Knowledge. PhD thesis, FCT-UNL, 1993.
Cristina Ribeiro and António Porto. Maximal intervals, an approach to temporal reasoning. In P. Barahona, L. Moniz Pereira, and A. Porto, editors, EPIA 91 — 5th Portuguese Conference on Artificial Intelligence. Springer Verlag, Lecture Notes on Artificial Intelligence 541, 1991.
Yoav Shoham. Reasonig about Change. The MIT Press, 1987.
Peter van Beek. Approximation algorithms for temporal reasoning. In Proceedings of the 11th International Joint Conference on Artificial Intelligence, pages 1291–1296, 1989.
Peter van Beek. Reasoning about qualitative temporal information. In Proceedings of the 8th National Conference on Artificial Intelligence, pages 728–734, 1990.
Marc Vilain and Henry Kautz. Constraint propagation algorithms for temporal reasoning. In Proceedings of the 5th National Conference on Artificial Intelligence, pages 377–382, 1986.
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© 1994 Springer-Verlag Berlin Heidelberg
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Ribeiro, C., Porto, A. (1994). Abduction in temporal reasoning. In: Gabbay, D.M., Ohlbach, H.J. (eds) Temporal Logic. ICTL 1994. Lecture Notes in Computer Science, vol 827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0013998
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DOI: https://doi.org/10.1007/BFb0013998
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