skip to main content
article
Free Access

Reasoning about temporal relations: a maximal tractable subclass of Allen's interval algebra

Published:03 January 1995Publication History
Skip Abstract Section

Abstract

We introduce a new subclass of Allen's interval algebra we call “ORD-Horn subclass,” which is a strict superset of the “pointisable subclass.” We prove that reasoning in the ORD-Horn subclass is a polynomial-time problem and show that the path-consistency method is sufficient for deciding satisfiability. Further, using an extensive machine-generated case analysis, we show that the ORD-Horn subclass is a maximal tractable subclass of the full algebra (assuming P ≠ NP). In fact, it is the unique greatest tractable subclass amongst the subclasses that contain all basic relations.

References

  1. ~ALLEN, J.F. 1983.Maintaining knowledge about temporal intervals. Commun. ACM 26, 11 ~(Nov.), 832-843. Google ScholarGoogle Scholar
  2. ~ALLEN, J.F. 1984. Towards a general theory of action and time. Artif. Int. 23, 2, 123-154. Google ScholarGoogle Scholar
  3. ~ALLEN, J.F. 1991. Temporal rcasoning and planning. In Reasomng about Plans, chap. 1. J. F. ~Allen, H. A. Kautz, R. N. Pelavin, and J. D. Tenenberg, eds. Morgan-Kaufmann, San Mateo, ~Calif., pp. 1-67. Google ScholarGoogle Scholar
  4. ~ALLEN, J. F., AND HAYES, P.J. 1985. A common-sense theory of time. In Proceedings of the ~9th International Joint Conference on Artificial Intelligence (Los Angeles, Calif., Aug.). Morgan- ~Kaufmann, San Mateo, Calif, pp. 528-531.Google ScholarGoogle Scholar
  5. ~ALLEN, J. F., AND KOOMEN, J.A. 1983. Planning using a temporal world model. In Proceedings ~of the 8th International Joint Conference on Artificial Intelligence. (Karlsruhe, Germany, Aug.). ~Morgan-Kaufmann, San Mateo, Calif., pp. 741-747.Google ScholarGoogle Scholar
  6. ~ANDRe,, E., GRAF, W., HEINSOHN, J., NEBEL, B., PROFITLICH, H.-J., RIST, T., AND WAHLSTER, W. ~1993. ~: Personalized plan-based presenter--Project Proposal. DFKI Document D-93-05, ~German Research Center for Artificial Intelligence (DFKI), Saarbriicken, May.Google ScholarGoogle Scholar
  7. ~DOWLING, W. F., AND GALLIER, J.H. 1984. Linear time algorithms for testing the satisfiability ~of propositional Horn formula. J. Logic Prog. 3, 267-284.Google ScholarGoogle Scholar
  8. ~FEINER, S. K., LITMAN, D. J., McKEowN, K. R., AND PASSONNEAU, R. J. 1993. Towards ~coordinated temporal multimedia presentation. In bztelligent Multi Media, M. Maybury, ed. ~AAAI Press, Menlo Park, Calif., pp. 139-147. Google ScholarGoogle Scholar
  9. FREKSA, C. 1992. Temporal reasoning based on semi-intervals. Artif Int. 54, 1-2, 199-227. Google ScholarGoogle Scholar
  10. FREUDER, E. C. 1978. Synthesizing constraint expressions. Commun. ACM 21, 11 (Nov.), ~958-966. Google ScholarGoogle Scholar
  11. ~GALLIER, J. H., AND RAATZ, S. 1985. Logic programming and graph rewriting. In Proceedings ~of Symposium on Logic Programming, IEEE Society, pp. 208-219.Google ScholarGoogle Scholar
  12. ~GEREVINI, A., AND SCHUBERT, L. 1993a. Complexity of temporal reasoning with disjunctions of ~inequalities. Tech. Rep. 9303-01. IRST, Trento, Italy, Jan.Google ScholarGoogle Scholar
  13. ~GEREVINI, A., AND SCHUBERT, L. 1993b. Efficient temporal reasoning through timegraphs. In ~Proceedings of the 13th International Joint Conference on Artificial Intelligence (Chambery, France, ~Aug.). Morgan-Kaufmann, San Mateo, Calif., pp. 648-654.Google ScholarGoogle Scholar
  14. ~GHALLAB, M., AND MOUNIR ALAOUI, A. 1989. Managing efficiently temporal relations through ~indexed spanning trees. In Proceedings of the llth International Joint Conference on Artificial ~Intelligence (Detroit, Mich., Aug.). Morgan-Kaufmann, San Mateo, Calif., pp. 1279-1303.Google ScholarGoogle Scholar
  15. ~GOLUMBIC, M. C., AND SHAMIR, R. 1992. Algorithms and complexity for reasoning about time. ~In Proceedings of the lOth National Conference of the American Association for Artificial ~Intelligence (San Jose, Calif., July). AAAI Press/MIT Press, Cambridge, Mass., pp. 741-747.Google ScholarGoogle Scholar
  16. ~GOLUMBIC, M. C., AND SHAMIR, R. 1993. Complexity and algorithms for reasoning about time: ~A graph-theoretic approach. J. ACM 40, 5 (Nov.), 1128-1133. Google ScholarGoogle Scholar
  17. ~HENSCHEN, L., AND WOS, L. 1974. Unit refutations and Horn sets. J. ACM 21, 4 (Oct.), ~590-605. Google ScholarGoogle Scholar
  18. ~KOUBARAKIS, M., MYLOPOULOS, J., STANLEY, M., AND BORGIDA, A. 1987. Teleos: Features and ~formalization. Tech. Rep. KRR-TR-89-4. Department of Computer Science, University of ~Toronto, Toronto, Ont., Canada.Google ScholarGoogle Scholar
  19. ~LADKIN, P. B. 1987. Models of axioms for time intervals. In Proceedings of the 6th Nattonal ~Conference of the American Association for Artificial Intelligence (AAAI-87) (Seattle, Wash., July), ~AAAI Press, Menlo Park, Calif., pp. 234-239.Google ScholarGoogle Scholar
  20. ~LADKIN, P. B., AND MADDUX, R. 1988. On binary constraint networks. Tech. Rep. KES.U.88.8. ~Kestrel Institute, Palo Alto, Calif.Google ScholarGoogle Scholar
  21. ~LADKIN, P. B., AND MADDUX, R. 1994. On binary constraint problems. J. ACM 41, 3 (May), ~435-469. Google ScholarGoogle Scholar
  22. ~LEVESQUE, H. J., AND BRACHMAN, R.J. 1987. Expressiveness and tractability in knowledge ~representation and reasoning. Comput. bzt. 3, 78-93.Google ScholarGoogle Scholar
  23. ~MACKWORTH, A.K. 1977. Consistency in networks of relations. Artif Int. 8, 99-118.Google ScholarGoogle Scholar
  24. ~MACKWORTH, A. K., AND FREUDER, E.C. 1985. The complexity of some polynomial network ~consistency algorithms for constraint satisfaction problems. Amf bzt. 25, 65-73. Google ScholarGoogle Scholar
  25. ~MONTANARI, U. 1974. Networks of constraints: Fundamental properties and applications to ~picture processing. Inf. Sci. 7, 95-132.Google ScholarGoogle Scholar
  26. ~NOKEL, K. 1989. Convex relations between time intervals. In Proceedings der 5. dJsterreichischen ~Artificial Intelligence-Tagung, J. Rettie and K. Leidlmair, eds. Springer-Verlag, Berlin, Heidel- ~berg, New York, pp. 298-302. Google ScholarGoogle Scholar
  27. ~N/SKEL, K. 1991. Temporally distributed symptoms in technical diagnosis. In Lecture Notes in ~Artificial Intelligence, vol. 517. Springer-Verlag, Berlin, Heidelberg, New York. Google ScholarGoogle Scholar
  28. ~SONG, F., AND COHEN, R. 1988. The interpretation of temporal relations in narrative. In ~Proceedings of the 7th National Conference of the American Association for Artificial Intelligence ~(Saint Paul, Minn., Aug.). AAAI Press, Menlo Park, Calif., pp. 745-750.Google ScholarGoogle Scholar
  29. ~VALD~Z-P~REZ, R.E. 1987. The satisfiability of temporal constraint networks. In Proceedings ~of the 6th National Conference of the American Association for Artificial Intelligence (Seattle, ~Wash., July). AAAI Press, Menlo Park, Calif., pp. 256-260.Google ScholarGoogle Scholar
  30. ~VAN BEEK, P. 1989. Approximation algorithms for temporal reasoning. In Proceedings of the ~l l th International Joint Conference on Artificial Intelligence (Detroit, Mich., Aug.). Morgan-Kauf- ~mann, San Mateo, Calif., pp. 1291-1296.Google ScholarGoogle Scholar
  31. ~VAN BEEK, P. 1990. Reasoning about qualitative temporal information. In Proceedings of the ~8th National Conference of the American Assoctation for Artificial Intelligence (Boston, Mass., ~Aug.). MIT Press, Cambridge, Mass., pp. 728-734.Google ScholarGoogle Scholar
  32. ~VAN BEEK, P., AND COHEN, R. 1990. Exact and approximate reasoning about temporal rela- ~tions. Comput. Int. 6. 132-144. Google ScholarGoogle Scholar
  33. ~VILAIN, M. B., AND KAUTZ, H. A. 1986. Constraint propagation algorithms for temporal ~reasoning. In Proceedings of the 5th National Conference of the American Association for Artificial ~bzteIligence (Philadelphia, Pa., Aug.). AAAI Press, Menlo Park, Calif., pp. 377-382.Google ScholarGoogle Scholar
  34. ~VILAIN, M. B., KAUTZ, H. A., AND VAN BEEK, P.G. 1989. Constraint propagation algorithms ~for temporal reasoning: A revised report. In Readings in Qualitatit,e Reasoning about Physical ~Systems, D. S. Weld and J. de Kleer, eds. Morgan-Kaufmann, San Mateo, Calif., pp. 373-381. Google ScholarGoogle Scholar
  35. ~WEIDA, R., AND LITMAN, D. 1992. Terminological reasoning with constraint networks and an ~application to plan recognition. In Principles of Knowledge Representation and Reasoning.' ~Proceedings of the 3rd Internattonal Conference (Cambridge, Mass., Oct.). B. Nebel, W. Swartout, ~and C. Rich, eds. Morgan-Kaufmann, San Mateo, Calif., pp. 282-293.Google ScholarGoogle Scholar

Index Terms

  1. Reasoning about temporal relations: a maximal tractable subclass of Allen's interval algebra

                Recommendations

                Comments

                Login options

                Check if you have access through your login credentials or your institution to get full access on this article.

                Sign in

                Full Access

                PDF Format

                View or Download as a PDF file.

                PDF

                eReader

                View online with eReader.

                eReader