Skip to main content
SpringerLink
Log in

Reasoning using inheritance from a mixture of knowledge and beliefs

  • Reasoning
  • Conference paper
  • First Online:
Knowledge Based Computer Systems (KBCS 1989)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 444))

Included in the following conference series:

  • 1953 Accesses

Abstract

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.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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 Alberta

    Google Scholar 

  2. 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, 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, 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, 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, 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, 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, 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, Jaime C. Default Reasoning and Inheritance Mechanisms on Type Hierarchies. SIGPLAN Notices 16(1): 107–109.

    Google Scholar 

  10. Etherington, D. W. and R. Reiter. On Inheritance Hierarchies with Exceptions. Proceedings of AAAI-83: 104–108, Washington, D.C.

    Google Scholar 

  11. Etherington, D. W. Formalizing Nonmonotonic Reasoning Systems. Artificial Intelligence 31: 41–85.

    Google Scholar 

  12. 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, D. W. A Semantics for Default Logic. Proceedings of the 10th International Joint Conference on Artificial Intelligence: 495–498, Milan.

    Google Scholar 

  14. 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, Scott E. NETL: A System for Representing and Using Real-World Knowledge. MIT Press, Cambridge, Massachusetts.

    Google Scholar 

  16. 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, 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, N. V. Associative Networks: Representation and Use of Knowledge by Computers (Nicholas V. Findler, ed.). Academic Press, New York.

    Google Scholar 

  19. 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, H., and J. Pearle. Sound defeasible inference. Technical report TR CSD870058, Cognitive Systems Lab, UCLA, Los Angeles, CA 90024-1596.

    Google Scholar 

  21. Halpern, J. Y. and M. O. Rabin. A Logic to Reason about Likelihood. Artificial Intelligence 32: 379–405.

    Google Scholar 

  22. 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, 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, 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, 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, J. T. Uncertainty and Probability. Proceedings of the 10th International Joint Conference on Artificial Intelligence, V. 1: 373–379, Milan.

    Google Scholar 

  27. 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, 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, N. Topics in Philosophical Logic. Reidel, Dordrecht.

    Google Scholar 

  30. Rosch, E., and C. Mervis. Family resemblances: Studies in the internal structure of categories. Cognitive Psychology 7: 573–605.

    Google Scholar 

  31. 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, G. A mathematical theory of evidence. Princeton University Press, Princeton, NJ.

    Google Scholar 

  33. 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, D. S. The Mathematics of Inheritance Systems. Morgan Kaufman Publishers, Los Altos, California.

    Google Scholar 

  35. 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 

Download references

Author information

Authors and Affiliations

  1. Institut Dalle Molle pour les, Etudes Semantiques & Cognitives, Route des Acacias, 54, 1227, Geneva, Switzerland

    Afzal Ballim

  2. Computing Research Lab, New Mexico State University, 88003, Las Cruces, NM, USA

    Sylvia Candelaria de Ram

  3. Centre for Systems Science, Simon Fraser University, V5A 1S6, Burnaby, British Columbia, Canada

    Dan Fass

Authors
  1. Afzal Ballim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sylvia Candelaria de Ram
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dan Fass
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

S. Ramani R. Chandrasekar K. S. R. Anjaneyulu

Rights and permissions

Reprints and permissions

Copyright information

© 1990 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ballim, A., de Ram, S.C., Fass, D. (1990). Reasoning using inheritance from a mixture of knowledge and beliefs. In: Ramani, S., Chandrasekar, R., Anjaneyulu, K.S.R. (eds) Knowledge Based Computer Systems. KBCS 1989. Lecture Notes in Computer Science, vol 444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0018396

Download citation

  • DOI: https://doi.org/10.1007/BFb0018396

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-52850-0

  • Online ISBN: 978-3-540-47168-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation