Skip to main content
Log in

Modality and interrupts

  • Published:
Journal of Automated Reasoning Aims and scope Submit manuscript

Abstract

We suggest that modal operators, in addition to their well-understood semantic role in declarative systems, also mark points at which these systems can be interrupted. We use this idea to describe an interruptible declarative system that gradually refines its responses to queries. Although initial responses may be in error, a correct answer will be provided if arbitrarily large computational resources are available. The ideas presented generalize existing work on stratification of logic programs and the treatment of floundered subgoals.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Agre, P. and Chapman, D.: Pengi: An implementation of a theory of activity, inProc. 6th National Conference on Artificial Intelligence, 1987, pp. 268–272.

  2. Apt, K., Blair, H., and Walker, A.: Towards a theory of declarative knowledge, in J. Minker (ed.),Foundations of Deductive Databases and Logic Programming, Morgan Kaufmann, 1987, pp. 89–142.

  3. Chan, D.: Constructive negation based on the completed database, inLogic Programming: Proc. 5th International Conference and Symposium, 1988, pp. 111–125.

  4. Chan, D.: An extension of constructive negation and its application in coroutining, inLogic Programming: Proc. 6th International Conference and Symposium, 1989.

  5. Chapman, D.: Planning for conjunctive goals,Artificial Intelligence 32 (1987), 333–377.

    Google Scholar 

  6. de Kleer, J.: An assumption-based truth maintenance system,Artificial Intelligence 28 (1986), 127–162.

    Google Scholar 

  7. Dean, T. and Boddy, M.: An analysis of time-dependent planning, inProc. 7th National Conference on Artificial Intelligence, 1988, pp. 49–54.

  8. Drummond, M.: Situated control rules. Technical report, NASA Ames Research Center, Moffett Field, Calif., 1988.

    Google Scholar 

  9. Drummond, M. and Bresina, J.: Anytime synthetic projection: Maximizing the probability of goal satisfaction, inProc. 8th National Conference on Artificial Intelligence, 1990, pp. 138–144.

  10. Elgot-Drapkin, J. J.: Step-logic: Reasoning Situated in Time, Ph.D. thesis, University of Maryland, College Park, Maryland, 1990.

    Google Scholar 

  11. Elgot-Drapkin, J. J. and Perlis, D.: Reasoning in time I: Basic concepts,J. Expt. Theor. Artif. Intell. 2 (1990), 75–98.

    Google Scholar 

  12. Etherington, D. and Crawford, J.: Toward efficient default reasoning, unpublished manuscript, 1992.

  13. Fitting, M. C.: Logic programming on a topological bilattice,Fundamenta Informatica 11 (1988), 209–218.

    Google Scholar 

  14. Gelfond, M.: On stratified autoepistemic theories, Technical report, University of Texas at El Paso, 1986.

  15. Ginsberg, M. L.: Multivalued logics: A uniform approach to reasoning in artificial intelligence,Computational Intelligence 4 (1988), 265–316.

    Google Scholar 

  16. Ginsberg, M. L.: A circumscriptive theorem prover,Artificial Intelligence 39 (1989), 209–230.

    Google Scholar 

  17. Ginsberg, M. L.: Bilattices and modal operators,Journal of Logic and Computation 1 (1990), 41–69.

    Google Scholar 

  18. Ginsberg, M. L.: Computational considerations in reasoning about action, inProc. 2nd International Conference on Principles of Knowledge Representation and Reasoning, Boston, 1991.

  19. Ginsberg, M. L.: The MVL theorem proving system,SIGART Bulletin 2(3) (1991), 57–60.

    Google Scholar 

  20. Ginsberg, M. L.: Negative subgoals with free variables,Journal of Logic Programming 11 (1991), 271–293.

    Google Scholar 

  21. Ginsberg, M. L.: User's guide to the MVL system, Technical report, University of Oregon, 1993.

  22. Ginsberg, M. L.: Approximate planning,Artificial Intelligence, 1995 (in press).

  23. Haas, A.: The case for domain-specific frame axioms, in F. M. Brown (ed.),The Frame Problem in Artificial Intelligence, Morgan Kaufmann, San Mateo, Calif., 1987.

    Google Scholar 

  24. Hanks, S. and McDermott, D.: Nonmonotonic logics and temporal projection,Artificial Intelligence 33 (1987), 379–412.

    Google Scholar 

  25. Kaelbling, L. P.: Goals as parallel program specifications, inProc. 7th National Conference on Artificial Intelligence, 1988, pp. 60–65.

  26. Konolige, K.: On the relation between default theories and autoepistemic logic,Artificial Intelligence 35 (1988), 343–382.

    Google Scholar 

  27. Konolige, K.: On the relation between default theories and autoepistemic logic (erratum),Artificial Intelligence 41 (1990), 115.

    Google Scholar 

  28. Kripke, S. A.: Semantical considerations on modal logic, in L. Linsky (ed.),Reference and Modality, Oxford University Press, London, 1971, pp. 63–72.

    Google Scholar 

  29. McCarthy, J.: Circumscription — a form of non-monotonic reasoning,Artificial Intelligence 13 (1980), 27–39.

    Google Scholar 

  30. McCarthy, J.: Applications of circumscription to formalizing common sense knowledge,Artificial Intelligence 28 (1986), 89–116.

    Google Scholar 

  31. Moore, R.: Semantical considerations on nonmonotonic logic,Artificial Intelligence 25 (1985), 75–94.

    Google Scholar 

  32. Myers, K. L. and Smith, D. E.: On the persistence of derived beliefs, inProc. 7th National Conference on Artificial Intelligence, 1988.

  33. Nilsson, N. J.: Action networks, Technical report, Stanford University, 1988.

  34. Perlis, D.: Non-monotonicity and real-time reasoning, inProc. 1984 Non-monotonic reasoning Workshop, New Paltz, N.Y., American Association for Artificial Intelligence, 1984, pp. 363–372.

    Google Scholar 

  35. Przymusinski, T.: On the declarative semantics of stratified deductive databases and logic program, in J. Minker (ed.),Foundations of Deductive Database and Logic Programming, Morgan Kaufmann, 1987, pp. 193–216.

  36. Reiter, R.: A logic for default reasoning,Artificial Intelligence 13 (1980), 81–132.

    Google Scholar 

  37. Reiter, R.: The frame problem in the situation calculus: A simple solution (sometimes) and a completeness result for goal regression, in V. Lifschitz (ed.),Artificial Intelligence and Mathematical Theory of Computation: Papers in Honor of John McCarthy, Academic Press, Boston, 1991, pp. 359–380.

    Google Scholar 

  38. Reiter, R. and Criscuolo, G.: On interacting defaults, inProc. 7th International Joint Conference on Artificial Intelligence, 1981, pp. 270–276.

  39. Rosenschein, S. J. and Kaelbling, L. P.: The synthesis of machines with provable epistemic properties, in J. Y. Halpern (ed.),Proc. 1986 Conference on Theoretical Aspects of Reasoning about Knowledge, Morgan Kaufmann, Los Altos, Calif., 1986, pp. 83–98.

    Google Scholar 

  40. Schoppers, M. J.: Universal plans for reactive robots in unpredictable domains, inProc. 10th International Joint Conference on Artificial Intelligence, 1987, pp. 1039–1046.

  41. Schubert, L. K.: Monotonic solution of the frame problem in the situation calculus, in H. E. Kyburg, Jr., R. P. Loui, and G. N. Carlson (eds),Knowledge Representation and Defeasible Reasoning, Kluwer, Boston, 1990, pp. 23–67.

    Google Scholar 

  42. Selman, B. and Kautz, H.: Knowledge compilation using Horn approximations, inProc. 9th National Conference on Artificial Intelligence, 1991, pp. 904–909.

  43. Skyrms, B.:Choice and Chance: An Introduction to Inductive Logic, Dickerson, 1966.

  44. Van Gelder, A.: The alternating fixpoint of logic programs with negation: Extended abstract, inProc. ACM SIGACT-SIGMOD Symposium on Principles of Database Systems, 1989.

  45. Van Gelder, A.: Negation as failure using tight derivations for general logic programs,J. Logic Program 6 (1989), 109–133.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ginsberg, M.L. Modality and interrupts. J Autom Reasoning 14, 43–91 (1995). https://doi.org/10.1007/BF00883930

Download citation

  • Received:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00883930

Key words

Navigation