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Representing Knowledge in A-Prolog

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Computational Logic: Logic Programming and Beyond

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2408))

Abstract

In this paper, we review some recent work on declarative logic programming languages based on stable models/answer sets semantics of logic programs. These languages, gathered together under the name of A-Prolog, can be used to represent various types of knowledge about the world. By way of example we demonstrate how the corresponding representations together with inference mechanisms associated with A-Prolog can be used to solve various programming tasks.

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References

  1. J. Alferes and L. Pereira. Reasoning With Logic Programming Springer Verlag, 1996.

    Google Scholar 

  2. K. Apt, H. Blair, and A. Walker. Towards a theory of declarative knowledge. In J. Minker, editor, Foundations of Deductive Databases and Logic Programming, pages 89–148. Morgan Kaufmann, San Mateo, CA., 1988.

    Google Scholar 

  3. K. Apt and M. Bezem. Acyclic programs. New Generation Computing, 9(3,4):335–365, 1991.

    Google Scholar 

  4. K. Apt and A. Pellegrini. On the occur-check free logic programs. ACM Transaction on Programming Languages and Systems, 16(3):687–726, 1994.

    Article  Google Scholar 

  5. C. Aravindan, J. Dix, and I. Niemela. Dislop: A research project on disjunctive logic programming, AI communications, 10(3/4):151–165.

    Google Scholar 

  6. F. Bacchus and F. Kabanza. Planning for Temporally Extended Goals. Annals of Mathematics and Artificial Intelligence, 22:1–2, 5–27.

    Google Scholar 

  7. M. Balduccini, M. Barry, M. Gelfond, M. Nogueira, and R. Watson An A-Prolog decision support system for the Space Shuttle. Lecture Notes in Computer Science-Proceedings of Practical Aspects of Declarative Languages’ 01, (2001), 1990:169–183

    Google Scholar 

  8. C. Baral and M. Gelfond. Logic programming and knowledge representation. Journal of Logic Programming, 19,20:73–148, 1994.

    Article  MathSciNet  Google Scholar 

  9. C. Baral, M. Gelfond, and A. Provetti. Representing Actions: Laws, Observations and Hypothesis. Journal of Logic Programming, 31(1–3):201–243, May 1997.

    Google Scholar 

  10. C. Baral, M. Gelfond, and O. Kosheleva. Expanding queries to incomplete databases by interpolating general logic programs. Journal of Logic Programming, vol. 35, pp 195–230, 1998.

    Article  MATH  MathSciNet  Google Scholar 

  11. C. Baral and M. Gelfond. Reasoning agents in dynamic domains. In J Minker, editor, Logic Based AI. pp. 257–279, Kluwer, 2000.

    Google Scholar 

  12. A. Bondarenko, P.M. Dung, R. Kowalski, F. Toni, An abstract, argumentation-theoretic approach to default reasoning. Artificial Intelligence 93(1–2) pages 63–101, 1997.

    Article  MATH  MathSciNet  Google Scholar 

  13. M. Cadoli, T. Eiter, and G. Gottlob. Default logic as a query language. IEEE Transactions on Knowledge and Data Engineering, 9(3), pages 448–463, 1997.

    Article  Google Scholar 

  14. W. Chen, T. Swift, and D. Warren. Efficient top-down computation of queries under the well-founded semantics. Journal of Logic Programming, 24(3):161–201, 1995.

    Article  MATH  MathSciNet  Google Scholar 

  15. P. Cholewinski. Stratified Default Logic. In Computer Science Logic, Springer LNCS 933, pages 456–470, 1995.

    Chapter  Google Scholar 

  16. P. Cholewinski. Reasoning with Stratified Default Theories. In Proc. of 3rd Int’l Conf. on Logic Programming and Nonmonotonic Reasoning, pages 273–286, 1995.

    Google Scholar 

  17. P. Cholewinski, W. Marek, and M. Truszczyński. Default Reasoning System DeReS. In Int’l Conf. on Principles of Knowledge Representation and Reasoning, 518–528. Morgan Kauffman, 1996.

    Google Scholar 

  18. A. Colmerauer, H. Kanoui, R. Pasero, and P. Russel. Un systeme de communication homme-machine en francais. Technical report, Groupe de Intelligence Artificielle Universitae de Aix-Marseille, 1973.

    Google Scholar 

  19. T. Eiter and G. Gottlob. Complexity aspects of various semantics for disjunctive databases. In Proc. of PODS-93, pages 158–167, 1993.

    Google Scholar 

  20. T. Eiter, N. Leone, C. Mateis., G. Pfeifer and F. Scarcello. A deductive system for nonmonotonic reasoning, Procs of the LPNMR’97, 363–373, 1997

    Google Scholar 

  21. T. Eiter, W. Faber, N. Leone. Declarative problem solving in DLV. In J Minker, editor, Logic Based AI, 79–103 Kluwer, 2000.

    Google Scholar 

  22. K. Clark. Negation as failure. In Herve Gallaire and J. Minker, editors, Logic and Data Bases, pages 293–322. Plenum Press, New York, 1978.

    Google Scholar 

  23. J. Delgrande and T. Schaub. Compiling reasoning with and about preferences into default logic. In Proc. of IJCAI 97, 168–174, 1997.

    Google Scholar 

  24. Y. Dimopoulos, B. Nebel, and J. Koehler. Encoding planning problems in nonmonotonic logic programs. Lecture Notes in Artificial Intelligence-Recent Advances in AI Planning, Proc. of the 4th European Conference on Planning, ECP’97, 1348:169–181, 1997

    Google Scholar 

  25. E. Erdem, V. Lifschitz, and M. Wong. Wire routing and satisfiability planning. Proc. of CL-2000, 822–836, 2000.

    Google Scholar 

  26. François Fages. Consistency of Clark’s completion and existence of stable models. Journal of Methods of Logic in Computer Science, 1(1):51–60, 1994.

    Google Scholar 

  27. M. Fitting. A Kripke-Kleene semantics for logic programs. Journal of Logic Programming, 2(4):295–312, 1985.

    Article  MathSciNet  MATH  Google Scholar 

  28. M. Gelfond and A. Gabaldon. Building a knowledge base: an example. Annals of mathematics and artificial Intelligence, 25:165–199.

    Google Scholar 

  29. M. Gelfond. On stratified autoepistemic theories. In Proc. AAAI-87, pages 207–211, 1987.

    Google Scholar 

  30. M. Gelfond and V. Lifschitz. The stable model semantics for logic programming. In R. Kowalski and K. Bowen, editors, Logic Programming: Proc. of the Fifth Int’l Conf. and Symp., pages 1070–1080, 1988.

    Google Scholar 

  31. M. Gelfond and V. Lifschitz. Classical negation in logic programs and disjunctive databases. New Generation Computing, pages 365–387, 1991.

    Google Scholar 

  32. M. Gelfond and V. Lifschitz. Representing Actions and Change by Logic Programs. Journal of Logic Programming, 17:301–323.

    Google Scholar 

  33. M. Gelfond, V Lifschitz, H. Przymusinska, and M. Truszczynski. Disjunctive defaults. In J. Allen, R. Fikes, and E. Sandewall, editors, Principles of Knowledge Representation and Reasoning: Proc. of the Second Int’l Conf., pages 230–237, 1991.

    Google Scholar 

  34. M. Gelfond, H. Przymusinska. On Consistency and Completeness of Autoepistemic Theories, Fundamenta Informaticae, vol. 16, Num. 1, pp. 59–92, 1992.

    MATH  MathSciNet  Google Scholar 

  35. M. Gelfond and V. Lifschitz. Action Languages. Electronic Transactions on Artificial Intelligence, Vol. 2, 193–210, 1998 http://www.ep.liu.se/ej/etai/1998/007

    MathSciNet  Google Scholar 

  36. M. Gelfond and T. Son. Reasoning with prioritized defaults. In J. Dix, L. M. Pereira, T. Przymusinski, editors, Lecture Notes in Artificial Intelligence, 1471, pp 164–224, 1998.

    Google Scholar 

  37. Y. Huang, H. Kautz and B. Selman. Control Knowledge in Planning: Benefits and Tradeoffs. 16th National Conference of Artificial Intelligence (AAAI’99), 511–517.

    Google Scholar 

  38. K. Inoue and C. Sakama. Negation as Failure in the Head. Journal of Logic Programming, 35(1):39–78, 1998.

    Article  MATH  MathSciNet  Google Scholar 

  39. C. Sakama and K. Inoue Prioritized Logic Programming and its Application to Commonsense Reasoning, Artificial Intelligence 123(1–2):185–222, Elsevier, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  40. A. C. Kakas, R. Kowalski, F. Toni, The Role of Abduction in Logic Programming, Handbook of Logic in Artificial Intelligence and Logic Programming 5, pages 235–324, D.M. Gabbay, C.J. Hogger and J.A. Robinson eds., Oxford University Press (1998).

    Google Scholar 

  41. M. Kaminski. A note on the stable model semantics of logic programs. Artificial Intelligence, 96(2):467–479, 1997.

    Article  MATH  MathSciNet  Google Scholar 

  42. R. Kowalski. Predicate logic as a programming language. Information Processing 74, pages 569–574, 1974.

    Google Scholar 

  43. R. Kowalski. Logic for Problem Solving. North-Holland, 1979.

    Google Scholar 

  44. C. Koch and N. Leone Stable model checking made easy. In proc. of IJCAI’99, 1999.

    Google Scholar 

  45. K. Kunen. Negation in logic programming. Journal of Logic Programming, 4(4):289–308, 1987.

    Article  MathSciNet  MATH  Google Scholar 

  46. K. Kunen. Signed data dependencies in logic programs. Journal of Logic Programming, 7(3):231–245, 1989.

    Article  MathSciNet  Google Scholar 

  47. V. Lifschitz. Restricted Monotonicity. In proc. of AAA-93, pages 432–437, 1993

    Google Scholar 

  48. V. Lifschitz. Foundations of logic programming. In Gerhard Brewka, editor, Principles of Knowledge Representation, pages 69–128. CSLI Publications, 1996.

    Google Scholar 

  49. V. Lifschitz. On the logic of causal explanation. Artificial Intelligence, 96:451–465, 1997.

    Article  MATH  MathSciNet  Google Scholar 

  50. V. Lifschitz and H. Turner. Splitting a logic program. In Pascal Van Hentenryck, editor, Proc. of the Eleventh Int’l Conf. on Logic Programming, pages 23–38, 1994.

    Google Scholar 

  51. V. Lifschitz, Two components of an action language. Annals of Math and AI, 21(2–4):305–320, 1997.

    MATH  MathSciNet  Google Scholar 

  52. V. Lifschitz and L. Tang and H. Turner, Nested expressions in logic programs. Annals of Math and AI, Vol. 25, pages 369–389, 1999

    MATH  MathSciNet  Google Scholar 

  53. V. Lifschitz. Action languages, Answer Sets, and Planning. In The Logic Programming Paradigm: a 25-Year Perspective, 357–353, Spring-Verlag, 1999.

    Google Scholar 

  54. W. Marek and V.S. Subrahmanian. The relationship between logic program semantics and non-monotonic reasoning. In G. Levi and M. Martelli, editors, Proc. of the Sixth Int’l Conf. on Logic Programming, pages 600–617, 1989.

    Google Scholar 

  55. W. Marek, and M. Truszczy’nski. Autoepistemic Logic. Journal of the ACM, 38, pages 588–619, 1991.

    Article  MATH  MathSciNet  Google Scholar 

  56. W. Marek, and M. Truszczyński. Stable models and an alternative logic programming paradigm. In The Logic Programming Paradigm: a 25-Year Perspective, 375–398, Spring-Verlag. 1999.

    Google Scholar 

  57. N. McCain and H. Turner. Causal theories of action and change. In Proc. of AAAI, pages 460–465, 1997.

    Google Scholar 

  58. N. McCain and H. Turner. Satisfiability planning with causal theories. In Proc. of KR, pages 212–223, 1998.

    Google Scholar 

  59. J. McCarthy. Programs with common sense. In Proc. of the Teddington Conference on the Mechanization of Thought Processes, pages 75–91, London, 1959. Her Majesty’s Stationery Office.

    Google Scholar 

  60. J. McCarthy and P. Hayes. Some philosophical problems from the standpoint of artificial intelligence. In B. Meltzer and D. Michie, editors, Machine Intelligence, volume 4, pages 463–502. Edinburgh University Press, Edinburgh, 1969.

    Google Scholar 

  61. J. Minker Overview of disjunctive logic programming. Annals of mathematics and artificial Intelligence, 12:1–24, 1994.

    Google Scholar 

  62. R. Moore. Semantical Considerations on Nonmonotonic Logic. Artificial Intelligence, 25(1):75–94, 1985.

    Article  MATH  MathSciNet  Google Scholar 

  63. D. Nelson. Constructible falsity. Journal of Symbolic Logic, 14:16–26, 1949.

    Article  MATH  MathSciNet  Google Scholar 

  64. I. Niemela. Logic Programming with stable model semantics as a constraint programming paradigm. In proceedings of the workshop on computational aspects of nonmonotonic reasoning, pp 72–79, Trento, Italy, 1998.

    Google Scholar 

  65. I. Niemela and P. Simons. Smodels-an implementation of the stable model and well-founded semantics for normal logic programs. In Proc. 4th international conference on Logic programming and non-monotonic reasoning, pages 420–429, 1997.

    Google Scholar 

  66. I. Niemela and P. Simons. Extending the Smodels system with cardinality and weight constraints. In J Minker, editor, Logic Based AI, pp. 491–522, Kluwer, 2000.

    Google Scholar 

  67. D. Pearce and G. Wagner. Reasoning with negative information 1-strong negation in logic programming. Technical report, Gruppe fur Logic, Wissentheorie and Information, Freie Universitat Berlin, 1989.

    Google Scholar 

  68. D. Pearce. From here to there: Stable negation in logic programming. In D. Gabbay and H. Wansing, editors. What is negation?, Kluwer, 1999.

    Google Scholar 

  69. T. Przymusinski. Perfect model semantics. In Proc. of Fifth Int’l Conf. and Symp., pages 1081–1096, 1988.

    Google Scholar 

  70. T. Przymusinski. Stable semantics for disjunctive programs. New generation computing, 9(3,4):401–425, 1991.

    Article  Google Scholar 

  71. E. Pednault. ADL: Exploring the middle ground between STRIPS and the situation calculus. In Proc. of KR89, pages 324–332, 1987.

    Google Scholar 

  72. R. Reiter. On closed world data bases. In H. Gallaire and J. Minker, editors, Logic and Data Bases, pages 119–140. Plenum Press, New York, 1978.

    Google Scholar 

  73. R. Reiter. A logic for default reasoning. Artificial Intelligence, 13(1,2):81–132, 1980.

    Article  MATH  MathSciNet  Google Scholar 

  74. M. Shanahan. Solving the frame problem: A mathematical investigation of the commonsense law of inertia. MIT press, 1997.

    Google Scholar 

  75. J. Schlipf. The expressive powers of the logic programming semantics. Journal of the Computer Systems and Science, 51, pages 64–86, 1995.

    Article  MATH  MathSciNet  Google Scholar 

  76. Simons, P. Extending the stable model semantics with more expressive rules. In 5th International Conference, LPNMR’99, 305–316.

    Google Scholar 

  77. T. Soininen and I. Niemela. Developing a declarative rule language for applications in program configuration. In practical aspects of declarative languages, LNCS 1551, pages 305–319, 1999.

    Chapter  Google Scholar 

  78. D. Seipel and H. Thone. DisLog-A system for reasoning in disjunctive deductive databases. In proc. of DAISD’94.

    Google Scholar 

  79. K. Stroetman. A Completeness Result for SLDNF-Resolution. Journal of Logic Programming, 15:337–355, 1993.

    MathSciNet  Google Scholar 

  80. V. Subrahmanian and C. Zaniolo. Relating stable models and AI planning domains. In L. Sterling, editor, Proc. ICLP-95, pages 233–247. MIT Press, 1995.

    Google Scholar 

  81. M. Thielscher. Ramification and causality. Artificial Intelligence, 89(1–2):317–364, 1997.

    Article  MATH  MathSciNet  Google Scholar 

  82. H. Turner. Signed logic programs. In Proc. of the 1994 International Symposium on Logic Programming, pages 61–75, 1994.

    Google Scholar 

  83. H. Turner. Representing actions in logic programs and default theories. Journal of Logic Programming, 31(1–3):245–298, May 1997.

    Google Scholar 

  84. H. Turner. Splitting a Default Theory, In Proc. of AAAI-96, pages 645–651, 1996.

    Google Scholar 

  85. M. van Emden and R. Kowalski. The semantics of predicate logic as a programming language. Journal of the ACM., 23(4):733–742, 1976.

    Article  MATH  Google Scholar 

  86. A. Van Gelder, K. Ross, and J. Schlipf. The well-founded semantics for general logic programs. Journal of ACM, 38(3):620–650, 1991.

    Article  MATH  Google Scholar 

  87. G. Wagner. Logic programming with strong negation and inexact predicates. Journal of Logic and Computation, 1(6):835–861, 1991.

    Article  MATH  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Texas Tech University, Lubbock, TX, 79409, USA

    Michael Gelfond

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  1. Michael Gelfond
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Editors and Affiliations

  1. Department of Computer Science, University of Cyprus, 75 Kallipoleos St., 1678, Nicosia, Cyprus

    Antonis C. Kakas

  2. Department of Computing, Imperial College of Science, Technology and Medicine, 180 Queen’s Gate, London, SW7 2BZ, UK

    Fariba Sadri

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Gelfond, M. (2002). Representing Knowledge in A-Prolog. In: Kakas, A.C., Sadri, F. (eds) Computational Logic: Logic Programming and Beyond. Lecture Notes in Computer Science(), vol 2408. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45632-5_16

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  • DOI: https://doi.org/10.1007/3-540-45632-5_16

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