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An efficient algorithm for temporal abduction

  • Vittorio Brusoni
  • Luce Console
  • Paolo Terenziani
  • Daniele Theseider Dupré
Automated Reasoning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1321)

Abstract

In this paper, we consider the following form of temporal abduction: given a domain theory where each explanatory formula is augmented with a set of temporal constraints on the atoms occurring in the formula, and given a set of observed atoms, with associated temporal constraints, the goal is the generation of a temporally consistent abductive explanation of the observations. Temporal abduction is the basis of many problem solving activities such as temporal diagnosis or reasoning about actions and events. This paper presents an efficient nondeterministic algorithm for temporal abduction which exploits the STP framework [8] in order to represent temporal information. In particular, we exploited some properties of STP, proved in

Keywords

Local Propagation Temporal Constraint Constraint Network Strict Constraint Abductive Reasoning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    J. Allen. Maintaining knowledge about temporal intervals. Communications of the ACM, 26:832–843, 1983.Google Scholar
  2. 2.
    V. Brusoni, L. Console, B. Pernici, and P. Terenziani. LaTeR: a general purpose manager of temporal information. In Methodologies for Intelligent Systems 8, pages 255–264. Lecture Notes in Computer Science 869, Springer Verlag, 1994.Google Scholar
  3. 3.
    V. Brusoni, L. Console, and P. Terenziani. On the computational complexity of querying bounds on differences constraints. Artificial Intelligence, 74(2):367–379, 1995.Google Scholar
  4. 4.
    V. Brusoni, L. Console, P. Terenziani, and D. Theseider Dupré. Characterizing temporal abductive diagnosis. In Proc. DX 95, Sixth Int. Workshop on Principles of Diagnosis, Goslar, 1995.Google Scholar
  5. 5.
    V. Brusoni, L. Console, P. Terenziani, and D. Theseider Dupré. A spectrum of definitions for temporal model-based diagnosis. Technical report, Dip. di Informatica, Università di Torino, 1997.Google Scholar
  6. 6.
    T. Bylander, D. Allemang, M. Tanner, and J. Josephson. The computational complexity of abduction. Artificial Intelligence, 49(1–3):25–60, 1991.Google Scholar
  7. 7.
    L. Console, L. Portinale, and D. Theseider Dupré. Using compiled knowledge to guide and focus abductive diagnosis. IEEE Transactions on Knowledge and Data Engineering, 8(5):690–706, 1996.Google Scholar
  8. 8.
    R. Dechter, I. Meiri, and J. Pearl. Temporal constraint networks. Artificial Intelligence, 49:61–95, 1991.Google Scholar
  9. 9.
    W. Hamscher and R. Davis. Diagnosing circuit with state: an inherently underconstrained problem. In Proc. AAAI 84, pages 142–147, 1984.Google Scholar
  10. 10.
    W. Long. Reasoning about state from causation and time in a medical domain. In Proc AAAI 83, pages 251–254, Washington, 1983.Google Scholar
  11. 11.
    W. Nejdl and J. Gamper. Harnessing the power of temporal abstractions in model-based diagnosis of dynamic systems. In Proc. 11th ECAI, pages 667–671, 1994.Google Scholar
  12. 12.
    D. Poole. Explanation and prediction: An architecture for default and abductive reasoning. Computational Intelligence, 5(2):97–110, 1989.Google Scholar
  13. 13.
    P. VanBeek. Temporal query processing with indefinite information. Artificial Intelligence in Medicine, 3:325–339, 1991.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Vittorio Brusoni
    • 1
  • Luce Console
    • 1
  • Paolo Terenziani
    • 1
  • Daniele Theseider Dupré
    • 1
  1. 1.Dipartimento di InformaticaUniversity di TorinoItaly

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