Skip to main content

Expanding SNePS capabilities with LORE

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 437))

Abstract

We briefly describe LORE, a logic with four values, the traditional truth values T and F, and two “Unknown” values, allowing to differentiate between knowing that nothing is known, and not knowing (with the available resources) whether it is known. A computer system based on LORE has the capability to remember all the paths followed during an attempt to answer a question. For each path, it records the used hypotheses (the hypotheses that constitute the path), the missing hypotheses (when the path did not lead to an answer), and why they were assumed missing. A number of examples of the use of LORE are discussed, and it is shown that SNePS capabilities can be expanded if LORE is accepted as the logic underlying its inferences.

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ackermann, R., An Introduction to Many-Valued Logics, (Dover Publications Inc., New York, 1967).

    Google Scholar 

  2. Anderson, A. and Belnap, N., Entailment: The Logic of Relevance and Necessity 1, (Princeton University Press, Princeton, NJ, 1975).

    Google Scholar 

  3. Belnap, N., A Useful Four-Valued Logic, in: G. Epstein and J.M. Dunn (Eds.), Modern Uses of Multiple-Valued Logic, (Reidel, Dordrecht, The Netherlands, 1977).

    Google Scholar 

  4. Bobrow, D. and Winograd, T., An Overview of KRL, a Knowledge Representation Language, Cognitive Science 1 (1) (1977) 3–46.

    Google Scholar 

  5. Donlon, G., Using Resource Limited Inference in SNePS, SNeRG Technical Note 10, Department of Computer Science, State University of New York at Buffalo, Buffalo, NY, 1982.

    Google Scholar 

  6. Driankov, D., Towards a Many-Valued Logic of Quantified Belief, Ph.D. Dissertation, Thesis 192, Department of Computer and Information Science, Linköping University, Linköping, Sweden, 1988.

    Google Scholar 

  7. Fagin, R. and Halpern, J., Belief, Awareness, and Limited Reasoning, Artificial Intelligence 34 (1) (1987) 39–76.

    Google Scholar 

  8. Hintikka, J., Impossible Possible Worlds Vindicated, J. Philosophical Logic 4 (1975) 475–484.

    Google Scholar 

  9. Holland, J., Holyoak, K., Nisbett, R. and Thagard, P., Induction: Processes of Inference, Learning, and Discovery, (MIT Press, Cambridge, Massachusetts, 1986).

    Google Scholar 

  10. Mamede, N., Pinto-Ferreira, C. and Martins, J., Reasoning with the Unknown, in: Martins and Morgado (Eds.), Proc. 4th Portuguese Conference on Artificial Intelligence, Lecture Notes in Artificial Intelligence 390, (Springer-Verlag, Berlin, 1989) 85–96.

    Google Scholar 

  11. Mamede, N. and Martins, J., Bringing Resources into Logic, (submitted for publication).

    Google Scholar 

  12. Martins, J., Reasoning in Multiple Belief Spaces, Ph.D. Dissertation, Technical Report 203, Department of Computer Science, State University of New York at Buffalo, Buffalo, NY, 1983.

    Google Scholar 

  13. Martins, J. and Shapiro, S., Hypothetical Reasoning, in: Sriram and Adey (Eds.), Applications of Artificial Intelligence in Engineering Problems: Proceedings of the First International Conference, (Springer-Verlag, Berlin, West Germany, 1986) 1029–1042.

    Google Scholar 

  14. Martins, J. and Shapiro, S., A Model for Belief Revision, Artificial Intelligence 35 (1) (1988) 25–79.

    Google Scholar 

  15. McKay, D., Monitors: Structuring Control Information, Thesis Proposal, Department of Computer Science, State University of New York at Buffalo, Buffalo, NY, 1981.

    Google Scholar 

  16. Minicozzi, E. and Reiter, R., A Note on Linear Resolution Strategies in Consequence-Finding, Artificial Intelligence 3 (1–4) (1972) 175–180.

    Google Scholar 

  17. Reichgelt, H., A Review of McDermott's “Critique of Pure Reason”, The European Journal on Artificial Intelligence 0 (1) (1987) 39–42.

    Google Scholar 

  18. Reiter R., and de Kleer, J., Foundations of Assumption-Based Truth Maintenance Systems: Preliminary Report, in: Proceedings AAAI-87, Seattle, Washington (1987) 183–188.

    Google Scholar 

  19. Shapiro, S., The SNePS Semantic Network Processing System, in: Findler (Ed.), Associative Networks: The Representation and Use of Knowledge by Computers, (Academic Press, New York, 1979) 179–203.

    Google Scholar 

  20. Shapiro, S., Personal Communication, 1989.

    Google Scholar 

  21. Shapiro, S. and The SNePS Implementation Group, SNePS-2 User's Manual, Department of Computer Science, State University of New York at Buffalo, Buffalo, NY, 1989.

    Google Scholar 

  22. Slagle, J., Chang, C. and Lee, R., Completeness Theorems for Semantic Resolution in Consequence-Finding in: Proceedings IJCAI-69, Washington, D.C., (1969) 281–285.

    Google Scholar 

  23. Srinivasan, C., The Architecture of Coherent Information Systems, IEEE Transactions on Computers C-25 (4) (1976) 390–402.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

D. Kumar

Rights and permissions

Reprints and permissions

Copyright information

© 1990 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mamede, N.J., Martins, J.P. (1990). Expanding SNePS capabilities with LORE. In: Kumar, D. (eds) Current Trends in SNePS — Semantic Network Processing System. SNePS 1989. Lecture Notes in Computer Science, vol 437. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0022081

Download citation

  • DOI: https://doi.org/10.1007/BFb0022081

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-52626-1

  • Online ISBN: 978-3-540-47081-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics