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Knowledge Representation: Features of Knowledge

  • James P. Delgrande
  • John Mylopoulos
Part of the Springer Study Edition book series (SSE)

Abstract

It is by now a cliché to claim that knowledge representation is a fundamental research issue in Artificial Intelligence (AI) underlying much of the research, and the progress, of the last fifteen years. And yet. it is difficult to pinpoint exactly what knowledge representation is, does, or promises to do. A thorough survey of the field by Ron Brachman and Brian Smith [Brachman & Smith 80] points out quite clearly the tremendous range in viewpoints and methodologies of researchers in knowledge representation. This paper is a further attempt to look at the field in order to examine the state of the art and provide some insights into the nature of the research methods and results. The distinctive mark of this overview is its viewpoint: that propositions encoded in knowledge bases have a number of important features, and these features serve, or ought to serve, as a basis for guiding current interest and activity in AI. Accordingly, the paper provides an account of some of the issues that arise in studying knowledge, belief, and conjecture, and discusses some of the approaches that have been adopted in formalizing and using some of these features in AI. The account is intended primarily for the computer scientist with little exposure to AI and Knowledge Representation, and who is interested in understanding some of the issues. As such, the paper concentrates on raising issues and sketching possible approaches to solutions. More technical details can be found in the work referenced throughout the paper.

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References

  1. American Association for Artificial Intelligence, Non-Monotonic Reasoning Workshop, New Paltz, New York, Oct. 1984Google Scholar
  2. A.R. Anderson and N.D. Belnap Jr., Entailment: The Logic of Relevance and Necessity, Vol. I, Princeton University Press, 1975.zbMATHGoogle Scholar
  3. D. Angluin and C.H. Smith, “A Survey of Inductive Inference: Theory and Methods”, Technical Report 250, Department of Computer Science, Yale University, 1982.Google Scholar
  4. A. Barr and J. Davidson, “Representation of Knowledge”, Stanford Heuristic Programming Project, Memo HPP-80–3. Stanford University. 1980.Google Scholar
  5. N.D. Belnap, “A Useful Four-Valued Logic” in Modern Uses of Multiple-Valued Logic, J.M. Dunn and G. Epsteineds., D. Reidel Pub. Co., 1975.Google Scholar
  6. W. Bibel, “First-Order Reasoning About Knowledge and Belief”, ATP-21-IX-83, Technical University of Munich, 1983.Google Scholar
  7. A. Borgida and T. Imielinski, “Decision Making in Committees — A Framework for Dealing with Inconsistency and Non-Monotonicity”, Workshop on Non-Monotonic Reasoning, New Paltz, 1984.Google Scholar
  8. A. Borgida, “Language Features For Flexible Handling Of Exceptions In Information Systems”. Transactions on Database Systems, to appear.Google Scholar
  9. R.J. Brachman, “On the Epistemological Status of Semantic Networks”, in Associative Networks: Representation and Use of Knowledge by Computers, N.V. Findler(ed.). Academic Press, 1979, pp 3–50.Google Scholar
  10. R.J. Brachman and H.J. Levesque, “Competence in Knowledge Representation” Proc. AAAI-82, Pittsburgh, 1982, pp 189–192.Google Scholar
  11. R.J. Brachman and H.J. Levesque, “The Tractability of Subsumption in Frame-Based Description Languages” Proc. AAAI-84, Austin, 1984, pp 34–37.Google Scholar
  12. R.J. Brachman and H.J. Levesque(eds.), Readings in Knowledge Representation, Morgan Kaufmann Publishers. Inc., 1985zbMATHGoogle Scholar
  13. R.J. Brachman and B.C. Smith(eds.), Special Issue on Knowledge Representation, SIGART Newsletter No. 70, Feb. 1980.Google Scholar
  14. P. Cheeseman. “In Defense of Probability”, Proc. IJCAI-85, Los Angeles. 1985. pp 1002–1009Google Scholar
  15. J.P. Delgrande, “A Foundational Approach to Conjecture and Knowledge in Knowledge Bases”, Ph.D. Thesis, Department of Computer Science, University of Toronto, 1985.Google Scholar
  16. A. P. Dempster, “A Generalization Of Bayesian Inference”, Journal of the Royal Statistical Society, Vol. 30, pp 205–247. 1968.zbMATHMathSciNetGoogle Scholar
  17. J. Doyle, “A Truth Maintenance System”, Artificial Intelligence 12, 1979, pp 231–272.Google Scholar
  18. J. Doyle and P. London, “A Selected Descriptor-Indexed Bibliography to the Literature on Belief Revision”, SIGART Newsletter #71, Apr. 1980. pp 7–23.Google Scholar
  19. F.I. Dretske, Knowledge and the Flow of Information, Bradford Books, the MIT Press, 1981.Google Scholar
  20. D. Dubois and H. Prade, “Combination and Propagation of Uncertainty with Belief Functions”, Proc. IJCAI-85, Los Angeles. 1985. pp 111–113Google Scholar
  21. R.O. Duda, P.E. Hart. N.J. Nilsson, and G.L. Sutherland. “Semantic Network Representations in Rule-Based Inference Systems”, in Pattern-Directed Inference Systems, D.A. Waterman and F. Hayes-Roth eds., Academic Press, 1978.Google Scholar
  22. D.W. Etherington, R.E. Mercer, and R. Reiter, “On the Adequacy of Predicate Circumscription for Closed-World Reasoning”, Computational Intelligence, Vol. 1, No. 1, 1985. pp 11–15.Google Scholar
  23. D.W. Etherington and R. Reiter, “On Inheritance Hierarchies with Exceptions”, Proc. AAAI83, 1983, pp 104–108.Google Scholar
  24. R. Fagin. J.Y. Halpern, and M.Y. Vardi, “A Model-Theoretic Analysis of Knowledge: Preliminary Report”. Proceedings of the Twenty-Fifth IEEE Symposium on Foundations of Computer Science, Florida, 1984.Google Scholar
  25. S.E. Fahlman. NETL: A System for Representing and Using Real-World Knowledge, MIT Press. 1979.zbMATHGoogle Scholar
  26. I. Goldstein and S. Papert, “Artificial Intelligence, Language, and the Study of Knowledge”, Cognitive Science, Vol. 1. No. 1,1977.Google Scholar
  27. N. Goodman, Fact, Fiction and Forecast, 3rd ed., Hackett Publishing Co., 1979.Google Scholar
  28. R.F. Hadley, “Two Solutions to Logical Omniscience: A Critique with an Alternative”. TR 85–21, School of Computing Science, Simon Fraser University, B.C., 1985Google Scholar
  29. J.Y. Halpern and Y.O. Moses. “A Guide to the Modal Logics of Knowledge and Belief: Preliminary Draft”, Proc IJCAI-85, Los Angeles, 1985.Google Scholar
  30. P. J. Hayes, “Some problems and Non-Problems in Representation Theory”. Proceedings AISB Summer Conference, 1974. pp 63–79.Google Scholar
  31. P.J. Hayes, “In Defense of Logic”, Proc. IJCAI-77, Cambridge. 1977, pp 559–565.Google Scholar
  32. P. J. Hayes, “The Naive Physics Manifesto”, Machine Intelligence 9, D. Michie(ed.), Edinburgh University Press, 1979, pp 243–270.Google Scholar
  33. C. Hewitt. “PLANNER: A Language for Proving Theorems in Robots”, Proceedings IJCAI-71, London, 1971.Google Scholar
  34. J. Hintikka, Knowledge and Belief: An Introduction to the Logic of the Two Notions, Cornell University Press. 1962.Google Scholar
  35. G.E. Hughes and M.J. Cresswell, An Introduction to Modal Logic, Methuen and Co., 1968.zbMATHGoogle Scholar
  36. D.J. Israel and R.J. Brachman, “Distinctions and Confusions: A Catalogue Raisonne”. Proceedings of the Seventh International Conference on Artificial Intelligence, Vancouver, B.C., 1981, pp 252–259.Google Scholar
  37. N.A. Khan and R. Jain. “Uncertainty Management in a Distributed Knowledge Based System”, Proc. IJCAI-85, Los Angeles, 1985, pp 318–320Google Scholar
  38. K. Konolige, “A Metalanguage Representation of Relational Databases for Deductive Question-Answering Systems”, Proceedings of the Seventh International Conference on Artificial Intelligence, Vancouver, B.C., 1981, pp 496–503.Google Scholar
  39. K. Konolige, “Circumscriptive Ignorance”, Proc. AAAI-82, Pittsburgh, 1982Google Scholar
  40. K. Konolige, “A Deductive Model of Belief”, Ph.D. Thesis, Department of Computer Science, Stanford University, 1984.Google Scholar
  41. B. Kramer and J. Mylopoulos, “Knowledge Representation: Knowledge Organization”, to appear.Google Scholar
  42. G. Lakemeyer, Internal Memo, Department of Computer Science, University of Toronto, 1984.Google Scholar
  43. H.J. Levesque, “A Formal Treatment of Incomplete Knowledge Bases”, Ph.D. thesis, Department of Computer Science, University of Toronto, 1981.Google Scholar
  44. H.J. Levesque, “A Logic of Implicit and Explicit Belief”. Proc. AAAI-84, Austin, 1984.Google Scholar
  45. D. Lewis. Counterfactuals, Harvard University Press. 1973.Google Scholar
  46. J. D. Lowrence, “Dependency-Graph Models of Evidence Support”. COINS technical report 82–26. University of Massachusetts at Amherst, 1982.Google Scholar
  47. G. McCalla and N. Cercone(eds.), IEEE Computer (Special Issue on Knowledge Representation) Vol. 16, No. 10, October 1983.Google Scholar
  48. J. McCarthy, “First Order Theories of Individual Concepts and Propositions”, in Machine Intelligence 9, D. Michie(ed.), Edinburgh University Press, 1979. pp 129–147.Google Scholar
  49. J. McCarthy, “Circumscription — A Form of Non-Monotonic Reasoning”, Artificial Intelligence 13, pp 27–39, 1980.CrossRefzbMATHMathSciNetGoogle Scholar
  50. J. McCarthy, “Applications of Circumscription to Formalizing Common Sense Knowledge”, Non-Monotonic Reasoning Workshop, New Paltz, New York, 1984. pp 295–324.Google Scholar
  51. J. McCarthy and P.J. Hayes, “Some Philosophical Problems from the Standpoint of Artificial Intelligence”, in Machine Intelligence 4, D. Michie and B. Meltzer(eds.), Edinburgh University Press. 1969, pp 463–502.Google Scholar
  52. J.P. Martins and S.C. Shapiro, “A Model for Belief Revision”, Non-Monotonic Reasoning Workshop, New Paltz. 1984.Google Scholar
  53. D. McDermott, “The Last Survey of Representation of Knowledge”, Proceedings AISB/GI Conference, 1978,206–221.Google Scholar
  54. D. McDermott, “Monmonotonic Logic II: Nonmonotonic Modal Theories” JACM 29, 1, 1982, pp 33–57CrossRefzbMATHMathSciNetGoogle Scholar
  55. D. McDermott and J. Doyle. “Non-Monotonic Logic I”, Artificial Intelligence 13, 1980, pp 41–72CrossRefzbMATHMathSciNetGoogle Scholar
  56. M. Minsky, “A Framework for Representing Knowledge” in The Psychology of Computer Vision, P.H. Winston(ed.), McGraw-Hill, 1975, pp 211–277.Google Scholar
  57. R. Montague, Formal Philosophy, Yale University Press, 1974.Google Scholar
  58. R.C. Moore, “Reasoning About Knowledge and Action”, Technical Note 284, Artificial Intelligence Centre, SRI International, 1980.Google Scholar
  59. R.C. Moore, “Semantical Considerations on Nonmonotonic Logic”. Proc. IJCAI-83, Karlsruhe, 1983, pp 272–279.Google Scholar
  60. R.C. Moore and G. Hendrix. “Computational Models of Beliefs and the Semantics of Belief-Sentences”, Technical Note 187, SRI International, Menlo Park, 1979.Google Scholar
  61. J. Mylopoulos and H.J. Levesque, “An Overview of Knowledge Representation” in On Conceptual Modelling, M.L. Brodie, J. Mylopoulos, and J.W. Schmidt(eds.), Springer-Verlag, 1984.Google Scholar
  62. A. Newell. “The Knowledge Level”, AI Magazine 2(2), 1981. pp 1–20.MathSciNetGoogle Scholar
  63. D.N. Osherson and E.E. Smith. “On the Adequacy of Prototype Theory as a Theory of Concepts”. Cognition 9, 1981. pp 35–58.CrossRefGoogle Scholar
  64. M. Papalaskaris and A. Bundy, “Topics for Circumscription”, Non-Monotonic Reasoning Workshop, New Paltz, New York, 1984. pp 355–362.Google Scholar
  65. H.E. Pople, “On the Mechanisation of Abductive Logic”, Proceedings of the Third International Conference on Artificial Intelligence, Stanford. Ca., 1973, pp 147–152.Google Scholar
  66. H. Prade, “A Computational Approach to Approximate and Plausible Reasoning with Applications to Expert Systems”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 7, No. 3, May 1985.Google Scholar
  67. H. Putnam, “Is Semantics Possible?” in Mind, Language and Reality: Philosophical Papers Volume II, Cambridge University Press, 1975, pp 215–271.CrossRefGoogle Scholar
  68. H. Putnam, “The ‘Corroboration’ of Theories”, in Mathematics, Matter, and Method: Philosophical Papers Volume I, 2nd ed., Cambridge University Press, 1979. pp 250–269.CrossRefGoogle Scholar
  69. W.V.O. Quine and J.S. Ullian. The Web of Belief, 2nd ed., Random House, 1978.Google Scholar
  70. R. Reiter, “On Closed World Data Bases”, in Logic and Databases, H. Gallaire and J. Minkereds., Plenum Press. 1978.Google Scholar
  71. R. Reiter, “A Logic for Default Reasoning”, Artificial Intelligence 13, 1980. pp 81–132.CrossRefzbMATHMathSciNetGoogle Scholar
  72. E. Rosch. “Principles of Categorisation” in Cognition and Categorisation, E. Rosch and B.B. Lloydseds., Lawrence Erlbaum Associates, 1978.Google Scholar
  73. I. Scheffler, The Anatomy of Inquiry: Philosophical Studies in the Theory of Science, Hackett Publishing Co., 1981.Google Scholar
  74. S.P. Schwartz(ed.), Naming, Necessity, and Natural Kinds, Cornell University Press. 1977.Google Scholar
  75. E.Y. Shapiro, “Inductive Inference of Theories from Facts”, Research Report 192, Department of Computer Science, Yale University, 1981.Google Scholar
  76. S. Shapiro and R. Bechtel, “The Logic of Semantic Networks”, TR-47, Department of Computer Science, Indiana University, 1976.Google Scholar
  77. E.H. Shortliffe, Computer-Based Medical Consultation: MYCIN, American Elsevier, 1976.Google Scholar
  78. T.R. Thompson, “Parallel Formulation of Evidential-Reasoning Theories” Proc. IJCAI-85, Los Angeles, 1985, pp 321–327Google Scholar
  79. D.S. Touretzky, “Implicit Ordering of Defaults in Inheritance Systems” Proc. AAAI-84, Austin, Texas, 1984. pp 322–325.Google Scholar
  80. J.K. Tsotsos, “Temporal Event Recognition: An Application to Left Ventriculat Performance”. Proc. IJCAI-81, Vancouver, 1981, pp 900–905Google Scholar
  81. Y. Vassiliou, “A Formal Treatment of Imperfect Information in Database Management”, Ph.D. Thesis, Department of Computer Science, University of Toronto, 1980.Google Scholar
  82. W.A. Woods, “What’s in a Link: Foundations for Semantic Networks” in Representation and Understanding, D.G. Bobrow and A. Collinseds., Academic Press, 1975.Google Scholar
  83. L.A. Zadeh,“Fuzzy Logic and Approximate Reasoning”, Synthese30, 1975, pp 407–428.CrossRefzbMATHGoogle Scholar
  84. L.A. Zadeh. “Commonsense Knowledge Representation Based on Fuzzy Logic”, IEEE Computer, Vol. 16, No. 10, October 1983,61–66.CrossRefGoogle Scholar
  85. S.W. Zucker, “Production Systems with Feedback”, in Pattern-Directed Inference Systems, D.A. Waterman and F. Hayes-Rotheds., Academic Press, 1978.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1987

Authors and Affiliations

  • James P. Delgrande
    • 1
    • 2
  • John Mylopoulos
    • 1
    • 3
  1. 1.Department of Computer ScienceUniversity of TorontoCanada
  2. 2.Department of Computing ScienceSimon Fraser UniversityBurnabyCanada
  3. 3.Canadian Institute for Advanced ResearchCanada

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