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Some approaches to reasoning with incomplete and changing information

  • Grigoris Antoniou
  • Mary-Anne Williams
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  • 219 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1359)

Keywords

Logic Program Logic Programming Belief Revision Default Theory Default Logic 
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References

  1. [1]
    C. Alchourrón, P. Gäxdenfors and D. Makinson. On the Logic of Theory Change: Partial Meet Functions for Contraction and Revision, Journal of Symbolic Logic, 50 (1985): 510–530.zbMATHMathSciNetCrossRefGoogle Scholar
  2. [2]
    J. Alferes and L.M. Pereira (1996). Reasoning with Logic Programming, LNAI 1111, Springer.Google Scholar
  3. [3]
    G. Antoniou and V. Sperschneider (1993). Computing Extensions of Nonmonotonic Logics. In Proc. 4th Scandinavian Conference on Artificial Intelligence, IOS Press.Google Scholar
  4. [4]
    G. Antoniou and V. Sperschneider (1994). Operational Concepts of Nonmonotonic Logics — Part 1: Default Logic. Artificial Intelligence Review 8: 3–16.zbMATHCrossRefGoogle Scholar
  5. [5]
    G. Antoniou (1997). Nonmonotonic Reasoning. The MIT Press.Google Scholar
  6. [6]
    D.E. Appelt and K. Konolige (1988). A Nonmonotonic Logic for Reasoning about Speech Acts and Belief Revision. In Reinfrank et. al. (eds.), Nonmonotonic Reasoning, Proc. 2nd International Workshop, Springer LNAI 346.Google Scholar
  7. [7]
    K.R. Apt and M.H. van Emden (1982). Contributions to the theory of logic programming. Journal of the ACM 29: 841–862.zbMATHCrossRefGoogle Scholar
  8. [8]
    K.R. Apt and R.N. Bol (1994). Logic Programming and Negation: A Survey. Journal of Logic Programming 19,20: 9–71.zbMATHMathSciNetCrossRefGoogle Scholar
  9. [9]
    P. Besnard (1989). An Introduction to Default Logic. Springer.Google Scholar
  10. [10]
    N. Bidoit and C. Froidevaux (1991). General logic databases and programs: default logic semantics and stratification. Information and Computation 91: 85–112.MathSciNetCrossRefGoogle Scholar
  11. [11]
    C. Boutilier. Revision Sequences and Nested Conditionals. In the Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann, 519–525, 1993.Google Scholar
  12. [12]
    G. Brewka (1991). Nonmonotonic Reasoning: Logical Foundations of Commonsense. Cambridge University Press.Google Scholar
  13. [13]
    G. Brewka (1994). Reasoning about Priorities in Default Logic. In Proc. of the 12th National Conference on Artificial Intelligence (AAAI-94). AAAI/MIT Press 1994,940–945.Google Scholar
  14. [14]
    J.P. Delgrande, T. Schaub and W.K. Jackson (1994). Alternative approaches to default logic. Artificial Intelligence 70 (1994): 167–237.zbMATHMathSciNetCrossRefGoogle Scholar
  15. [15]
    D. Dubois and H. Prade. Possibilistic Logic. Handbook of Logic in Artificial Intelligence and Logic Programming, Volume3, Nonmonotonic Reasoning and uncertain Reasoning, Gabbay, D., Hogger, C., and Robinson, J. (eds), Claredon Press, Oxford, 1994.Google Scholar
  16. [16]
    D. Etherington (1987a). Formalizing Nonmonotonic Reasoning Systems. Artificial Intelligence 31 (1987): 41–85.zbMATHMathSciNetCrossRefGoogle Scholar
  17. [17]
    D. Etherington (1987b). Reasoning with Incomplete Information. Pitman 1987.Google Scholar
  18. [18]
    A. Fuhrmann. Theory Contraction through Base Contraction. Journal of Philosophical Logic, 20 (1991): 175–203.zbMATHMathSciNetCrossRefGoogle Scholar
  19. [19]
    P. Gärdenfors. Knowledge in Flux: Modeling the Dynamics of Epistemic States, Bradford Books, The MIT Press, Cambridge Massachusetts, 1988.Google Scholar
  20. [20]
    P. Gäsdenfors and D. Makinson. Revisions of Knowledge Systems using Epistemic Entrenchment. In the Proceedings of the Second Conference on Theoretical Aspects of Reasoning about Knowledge, 83–96, 1988.Google Scholar
  21. [21]
    P. Gärdenfors and H. Rott. Belief Revision.Handbook of Logic in Artificial Intelligence and Logic Programming Volume IV: Epistemic and Temporal Reasoning, Chapter 4.2, Gabbay, D., Hogger, C., and Robinson, J. (eds), Claredon Press, (in press).Google Scholar
  22. [22]
    M. Gelfond (1987). On Stratified Autoepistemic Theories. In Proc. American National Conference on Artificial Intelligence.Google Scholar
  23. [23]
    M. Gelfond and V. Lifschitz (1988). The stable semantics for logic programs. In Proceedings of the 5th International Symposium on Logic Programming. MIT Press.Google Scholar
  24. [24]
    M. Gelfond and H. Przymusinska (1989). Formalization of Inheritance Reasoning in Autoepistemic Logic. Fundamenta Informaticae 13(4): 403–444.MathSciNetGoogle Scholar
  25. [25]
    M. Gelfond and V. Lifschitz (1990). Logic programs with classical negation. In Proceedings 7th International Conference on Logic Programming. MIT Press.Google Scholar
  26. [26]
    M. Gelfond and H. Przymusinska (1992). On Consistency and Completeness of Autoepistemic Theories. Fundamenta Informaticae 16:59–92.zbMATHMathSciNetGoogle Scholar
  27. [27]
    A. Grove. Two Modellings for Theory Change. Journal of Philosophical Logic, 17 (1988): 157–170.zbMATHMathSciNetCrossRefGoogle Scholar
  28. [28]
    S.O. Hansson. New Operators for Theory Change. Theoria, 55 (1989): 115–132.MathSciNetGoogle Scholar
  29. [29]
    H. Katsuno and A.O. Mendelzon. On the Difference between Updating a Knowledge Database and Revising it. In Belief Revision, Gärdenfors, P. (ed), Cambridge Press, Cambridge, 1992.Google Scholar
  30. [30]
    K. Konolige (1988a). On the relation between default and autoepistemic logic. Artificial Intelligence 35:343–382; see also Errata, Artificial Intelligence 41:115, 1989.zbMATHMathSciNetCrossRefGoogle Scholar
  31. [31]
    K. Konolige (1988b). Hierarchic Autoepistemic Theories for Nonmonotonic Reasoning: Preliminary Report. In Reinfrank et. al. (eds.), Nonmonotonic Reasoning, Proc. 2nd International Workshop, Springer LNAI 346.Google Scholar
  32. [32]
    K. Konolige (1991). Quantifying in Autoepistemic Logic. Fundamenta Informaticae 15(3–4).Google Scholar
  33. [33]
    S. Kraus, D. Lehmann and M. Magidor (1990). Nonmonotonic reasoning, preferential models and cumulative logics. Artificial Intelligence 44: 167–207.zbMATHMathSciNetCrossRefGoogle Scholar
  34. [34]
    J. Lang (1997). Possibilistic Logic: Algorithms and Complexity. in J.Kohlas and S. Moral (eds), Handbook of Algorithms for Uncertainty and Defeasible Reasoning, Kluwer Academic Publishers.Google Scholar
  35. [35]
    D. Lehmann. Belief Revision, Revised. In the Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, 1995.Google Scholar
  36. [36]
    J. Lloyd (1987). Foundations of logic programming, 2nd ed., Springer.Google Scholar
  37. [37]
    W. Lukaszewicz (1988). Considerations on Default Logic. Computational Intelligence 4 (1988): 1–16.MathSciNetGoogle Scholar
  38. [38]
    W. Lukaszewicz (1990). Non-Monotonic Reasoning — Formalization of commonsense reasoning. Ellis Horwood.Google Scholar
  39. [39]
    D. Makinson. On the Status of the Postulate of Recovery in the Logic of Theory Change. Journal of Philosophical Logic, 16 (1987): 383–394.zbMATHMathSciNetCrossRefGoogle Scholar
  40. [40]
    D. Makinson (1994). General patterns in nonmonotonic reasoning. In Handbook of Logic in Artificial Intelligence and Logic Programming Vol. 3, Oxford University Press, 35–110.Google Scholar
  41. [41]
    W. Marek (1989). Stable Theories in Autoepistemic Logic. Fundamenta Informaticae 12:243–254.zbMATHMathSciNetGoogle Scholar
  42. [42]
    W. Marek and M. Truszczynski (1989). Relating Autoepistemic Logic and Default Logic. Proc. 1st International Conference on Knowledge Representation and Reasoning.Google Scholar
  43. [43]
    W. Marek and M. Truszczynski (1993). Nonmonotonic Logic — Context-Dependent Reasoning, Springer.Google Scholar
  44. [44]
    J. McCarthy (1980). Circumscription — A Form of Non-Monotonic Reasoning. Artificial Intelligence 13: 27–39.zbMATHMathSciNetCrossRefGoogle Scholar
  45. [45]
    A. Mikitiuk and M. Truszczynski (1995). Constrained and rational default logics. In Proc. 14th International Joint Conference on Artificial Intelligence, Morgan Kaufmann, 1509–1515.Google Scholar
  46. [46]
    R.C. Moore (1984). Possible-world semantics for autoepistemic logic. In Proc. Non-monotonic reasoning Workshop, New Paltz.Google Scholar
  47. [47]
    R.C. Moore (1985). Semantical Considerations on Nonmonotonic Logic. Artificial Intelligence 25:75–94.zbMATHMathSciNetCrossRefGoogle Scholar
  48. [48]
    A. Nayak. Iterated Belief Change Based on Epistemic Entrenchment. Erkenntnis 4 (1994): 353–390.MathSciNetCrossRefGoogle Scholar
  49. [49]
    B. Nebel. A Knowledge Level Analysis of Belief Revision. In Principles of Knowledge epresentation and Reasoning: Proceedings of the First International Conference, Morgan Kaufmann, San Mateo, CA, 301–311, 1989.Google Scholar
  50. [50]
    D. Poole (1994). Default Logic. In Handbook of Logic in Artificial Intelligence and Logic Programming, Oxford University Press.Google Scholar
  51. [51]
    R. Reiter (1980). A Logic for Default Reasoning. Artificial Intelligence 13:81–132.zbMATHMathSciNetCrossRefGoogle Scholar
  52. [52]
    K. Ross (1992). A procedural semantics for well-founded negationin logic programs. Journal of Logic Programming 13: 1–22.zbMATHMathSciNetCrossRefGoogle Scholar
  53. [53]
    H. Rott. A Nonmonotonic Conditional Logic for Belief Revision I. In A. Fuhrmann and M. Morreau (eds), The Logic of Theory Change, Springer-Verlag, LNAI 465, Berlin, 135–183, 1991.CrossRefGoogle Scholar
  54. [54]
    T. Schaub (1992). On Constrained Default Theories. In Proc. 10th European Conference on Artificial Intelligence, Wiley 1992, 304–308.Google Scholar
  55. [55]
    V. Sperschneider and G. Antoniou (1991). Logic: A Foundation for Computer Science, Addison-Wesley.Google Scholar
  56. [56]
    W. Spohn. Ordinal Conditional Functions: A Dynamic Theory of Epistemic States. In Harper, W.L., and Skyrms, B. (eds), Causation in decision, belief change, and statistics, II, Kluwer Academic Publishers, p105–134, 1988.Google Scholar
  57. [57]
    R. Stalnaker. A theory of conditionals. in Recher, N. (ed), Studies in Logical Theory, Blackwell, Oxford, 98–112, 1968.Google Scholar
  58. [58]
    H. Tamaki and T. Sato (1986). OLD Resolution and Tabulation. In Proceedings of the Third International Conference on Logic Programming. Springer.Google Scholar
  59. [59]
    A. van Gelder, K.A. Ross and J.S. Schlipf (1991). The Well-Founded Semantics for General Logic Programs. Journal of the ACM 38,3: 620–650.zbMATHCrossRefGoogle Scholar
  60. [60]
    D.S. Warren (1991). Computing the Well-Founded Semantics of Logic Programs. Technical Report 91/12, Computer Science Department, SUNY at Stony Brook.Google Scholar
  61. [61]
    M.A. Williams. On the Logic of Theory Base Change. In Logics in Artificial Intelligence, C. MacNish, D. Pearce and L.M. Pereira (eds), LNCS No 835, 86–105, Springer Verlag, 1994.Google Scholar
  62. [62]
    M-A. Williams (1996). Towards a Practical Approach to Belief Revision: Reason-Based Change, Luigia Carlucci Aiello and C. Shapiro (eds), Proceedings of the Fifth International Joint Conference on Principles of Knowledge Representation and Reasoning, Morgan Kaufmann Publishers, 412–421.Google Scholar
  63. [63]
    M-A. Williams (1997). Anytime Belief Revision, in the Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann.Google Scholar
  64. [64]
    M. Winslett. Reasoning about action using possible models approach. In the Proceedings of the National Conference on Artificial Intelligence (AAAI), 89–93, 1988.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Grigoris Antoniou
    • 1
  • Mary-Anne Williams
    • 2
  1. 1.CIT, Griffith UniversityNathanAustralia
  2. 2.Department of ManagementThe University of NewcastleCallaghanAustralia

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