Reasoning using inheritance from a mixture of knowledge and beliefs

  • Afzal Ballim
  • Sylvia Candelaria de Ram
  • Dan Fass
Part of the Lecture Notes in Computer Science book series (LNCS, volume 444)


Certain inadequacies of homogeneous inheritance systems have caused an interest in heterogeneous inheritance systems. Heterogeneous representations allow for mixing of ‘known’ relations (inherited through ‘strict’ links) and what is ‘believed’ (inherits through ‘defeasible’ links). However, few well-founded systems have been proposed and heterogeneous systems have been considered to be not yet well understood. This paper presents a theory and implementation of a heterogeneous inheritance system. The principles of the system are that (i) rules of composition allow paths to be considered as single links (effective relationships), and (ii) rules of comparison allow selection of those effective relationships which state the most definite, specific information. These rules are enumerated and discussed, then an implementation of the theory is shown. An example of the operation of the system is explained in detail. Related recent work is noted.


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  1. [Bacchus 1987]
    Bacchus, F. A Heterogeneous Inheritance System Based on Probabilities. Tech. Report 87-03, Alberta Center for Machine Intelligence and Robotics, Dept. of Computer Science, Univ. of AlbertaGoogle Scholar
  2. [Ballim et al, 1988a, b]
    Ballim, A., D. Fass, and S. Candelaria de Ram. Resolving a clash of intuitions: Utilizing strict and defeasible information in inheritance systems. Memoranda in Computer and Cognitive Science, MCCS-88-119, Computing Research Laboratory, New Mexico State University, Las Cruces, NM 88003, USA.Google Scholar
  3. [Ballim et al, 1988a, b]
    Ballim, A., S. Candelaria de Ram, and D. Fass. Some Foundations for Inheritance Systems. ISSCO working paper 55, ISSCO, 54 Rte des Acacias, Geneva, Suisse.Google Scholar
  4. [Brachman, 1977]
    Brachman, Ronald J. What's in a Concept: Structural Foundations for Semantic Networks. International Journal for Man-Machine Studies 9: 127–152.Google Scholar
  5. [Brachman, 1979]
    Brachman, Ronald J. On the Epistemological Status of Semantic Networks, p. 3–50 in Associative Networks: Representation and Use of Knowledge by Computers (Nicholas V. Findler, ed.). Academic Press, New York.Google Scholar
  6. [Brachman, 1983]
    Brachman, Ronald J. What ISA Is and Isn't: An Analysis of Taxonomic Links in Semantic Networks. IEEE Computer 16(10): 30–36.Google Scholar
  7. [Brachman, 1985]
    Brachman, R. J. I Lied About the Trees (or, Defaults and Definitions in Knowledge Representation). AI Magazine 6(3): 80–93.Google Scholar
  8. [Candelaria de Ram, 1982]
    Candelaria de Ram, S. Utilizing Fuzziness: Toward a Model of Language Dynamics, invited applications paper, First North American Workshop on Fuzzy Information Processing, Ogden, Utah.Google Scholar
  9. [Carbonell, 1981]
    Carbonell, Jaime C. Default Reasoning and Inheritance Mechanisms on Type Hierarchies. SIGPLAN Notices 16(1): 107–109.Google Scholar
  10. [Etherington and Reiter, 1983]
    Etherington, D. W. and R. Reiter. On Inheritance Hierarchies with Exceptions. Proceedings of AAAI-83: 104–108, Washington, D.C.Google Scholar
  11. [Etherington, 1987a, b, c, d]
    Etherington, D. W. Formalizing Nonmonotonic Reasoning Systems. Artificial Intelligence 31: 41–85.Google Scholar
  12. [Etherington, 1987a, b, c, d]
    Etherington, D. W. 1987. Relating Default Logic and Circumscription. Proceedings of the 10th International Joint Conference on Artificial Intelligence: 489–494, Milan.Google Scholar
  13. [Etherington, 1987a, b, c, d]
    Etherington, D. W. A Semantics for Default Logic. Proceedings of the 10th International Joint Conference on Artificial Intelligence: 495–498, Milan.Google Scholar
  14. [Etherington, 1987a, b, c, d]
    Etherington, D. W. More on Inheritance Hierarchies with Exceptions: Default Theories and Inferential Distance. Proceedings of AAAI '87: 352–357, Seattle.Google Scholar
  15. [Fahlman, 1979]
    Fahlman, Scott E. NETL: A System for Representing and Using Real-World Knowledge. MIT Press, Cambridge, Massachusetts.Google Scholar
  16. [Falkenhainer et al, 1986]
    Falkenhainer, B., K. Forbus, and D. Gentner. The Structure-Mapping Engine. Proceedings of the Fifth National Conference on Artificial Intelligence: 272–277, Philadelphia.Google Scholar
  17. [Fass, 1987]
    Fass, Dan C. Semantic Relations, Metonymy, and Lexical Ambiguity Resolution: A Coherence-Based Account. Proceedings of the 9th Annual Cognitive Science Society Conference: 575–586. University of Washington, Seattle.Google Scholar
  18. [Findler, 1979]
    Findler, N. V. Associative Networks: Representation and Use of Knowledge by Computers (Nicholas V. Findler, ed.). Academic Press, New York.Google Scholar
  19. [Fox, 1979]
    Fox, M. S. 1979. On Inheritance in Knowledge Representation. Proceedings of the 6th International Joint Conference on Artificial Intelligence (IJCAI-79): 282–284, Tokyo, Japan.Google Scholar
  20. [Geffner and Pearle, 1987]
    Geffner, H., and J. Pearle. Sound defeasible inference. Technical report TR CSD870058, Cognitive Systems Lab, UCLA, Los Angeles, CA 90024-1596.Google Scholar
  21. [Halpern and Rabin, 1987]
    Halpern, J. Y. and M. O. Rabin. A Logic to Reason about Likelihood. Artificial Intelligence 32: 379–405.Google Scholar
  22. [Horty and Thomason, 1988]
    Horty, J. F. and R. H. Thomason. Mixing Strict and Defeasible Inheritance. Proceedings of the 7th National Conference on Artificial Intelligence (AAAI-88): 427–432, St. Paul, Minnesota.Google Scholar
  23. [Horty et al, 1987]
    Horty, J. F., R. H. Thomason, and D. S. Touretzky. A Skeptical Theory of Inheritance in Nonmonotonic Semantic Networks. Proceedings of AAAI '87: 358–363, Seattle.Google Scholar
  24. [Moore and Hutchins, 1981]
    Moore, G. W. and G. M. Hutchins. A Hintikka possible worlds model for certainty levels in medical decision making. Synthese 48: 87–119.Google Scholar
  25. [Nado and Fikes, 1987]
    Nado, R. and R. Fikes. Semantically Sound Inheritance for a Formally Defined Frame Language with Defaults. Proceedings of AAAI '87: 443–448, Seattle.Google Scholar
  26. [Nutter, 1987a, b]
    Nutter, J. T. Uncertainty and Probability. Proceedings of the 10th International Joint Conference on Artificial Intelligence, V. 1: 373–379, Milan.Google Scholar
  27. [Nutter, 1987a, b]
    Nutter, J. T. Default Reasoning. p. 840–848 in Encyclopedia of Artificial Intelligence (S. C. Shapiro, ed.). John Wiley, New York.Google Scholar
  28. [Peters and Shapiro, 1987]
    Peters, S. L. and S. C. Shapiro. A Representation for Natural Category Systems. Proceedings of the 10th International Joint Conference on Artificial Intelligence: 140–146, Milan.Google Scholar
  29. [Rescher, 1968]
    Rescher, N. Topics in Philosophical Logic. Reidel, Dordrecht.Google Scholar
  30. [Rosch and Mervis, 1975]
    Rosch, E., and C. Mervis. Family resemblances: Studies in the internal structure of categories. Cognitive Psychology 7: 573–605.Google Scholar
  31. [Sandewall, 1986]
    Sandewall, Eric. Nonmonotonic Inference Rules for Multiple Inheritance with Exceptions. Proceedings of IEEE V 74 (No. 10; October, 1986): 1345–1353.Google Scholar
  32. [Shafer, 1976]
    Shafer, G. A mathematical theory of evidence. Princeton University Press, Princeton, NJ.Google Scholar
  33. [Shastri and Feldman, 1985]
    Shastri, L. and J. A. Feldman. Evidential Reasoning in Semantic Networks: A Formal Theory. Proceedings of the 9th International Joint Conference On Artificial Intelligence: 465–474, Los Angeles.Google Scholar
  34. [Touretzky, 1986]
    Touretzky, D. S. The Mathematics of Inheritance Systems. Morgan Kaufman Publishers, Los Altos, California.Google Scholar
  35. [Touretzky et al, 1987]
    Touretzky, D. S., J. F. Horty, and R. H. Thomason. A Clash of Intuitions: The Current State of Nonmonotonic Multiple Inheritance Systems. Proceedings of the 10th International Joint Conference on Artificial Intelligence: 476–482, Milan.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1990

Authors and Affiliations

  • Afzal Ballim
    • 1
  • Sylvia Candelaria de Ram
    • 2
  • Dan Fass
    • 3
  1. 1.Institut Dalle Molle pour lesEtudes Semantiques & CognitivesGenevaSwitzerland
  2. 2.Computing Research LabNew Mexico State UniversityLas CrucesUSA
  3. 3.Centre for Systems ScienceSimon Fraser UniversityBurnabyCanada

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