Skip to main content

Rewrite semantics for production rule systems: Theory and applications

  • Session 7A
  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1104))

Abstract

Production rules are a fundamental computational paradigm in artificial intelligence, perhaps being best known as the basis for expert systems. However, to this point there has been little formal study of their properties as a method of deduction. In this paper we initiate such a study by presenting a rewrite semantics for production rule systems. Such a formalization is both interesting per se as a paradigm for deduction and also useful in providing a formal framework for analyzing properties of production rule systems. We show how to represent production rules as rewrite rules operating on collections of atoms, thereby allowing us to import techniques from equational reasoning (confluence checking and completion, critical pair criteria, termination orderings, etc.). An interesting feature of this representation is the use of symbolic constraints to represent the negation-as-failure interpretation of negative conditions in production rules. Practical applications of this theory provide for the development of a comprehensive environment for verifying and validating the correctness of production rule systems, as well as the development of convergent production rule systems for special applications such as parallel evaluation and real-time control.

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. R. Plant (Ed). Validation and Verification of Knowledge-Based Systems. Proceedings of AAAI-94 workshop, Seattle, Washington (1994).

    Google Scholar 

  2. A. Aiken, J. Widom, and J. Hellerstein. Behavior of Database Production Rules: Termination, Confluence, and Observable Determinism. In Proceedings of ACM SIGMOD, pp. 59–68, 1992.

    Google Scholar 

  3. L. Bachmair. Canonical Equational Proofs. Birkhäuser Boston, Inc., Boston, MA (1991).

    Google Scholar 

  4. B. Buchanan and E. Shortliffe. Rule-Based Expert Systems.Addison-Wesley, Reading, MA (1984).

    Google Scholar 

  5. H. Comon and P. Lescanne. Equational Problems and Disunification. Journal of Symbolic Computation, 7 (1989) 371–425.

    Google Scholar 

  6. N. Dershowitz and J.-P. Jouannaud. Rewrite Systems. In Handbook of Theoretical Computer Science, Volume B, J.V. Leeuwen (Ed.), Elsevier (1990), pp. 243–320.

    Google Scholar 

  7. M. Ayel and M.-C. Rousset (Eds). Proceedings of the European Symposium on the Validation and Verification of Knowledge-Based Systems. Université de Savoie, Chambéry, France (1995).

    Google Scholar 

  8. C. L. Forgy. OPS5 User's Manual. Technical Report CMU-CS-81-135, Department of Computer Science, CMU, 1981.

    Google Scholar 

  9. Herbert Groiss. A Formal Semantics for mOPS5. In Proceedings of the Seventh IEEE International Conference on Tools with Artificial Intelligence, Washington, DC, November 1995.

    Google Scholar 

  10. C. Kirchner, H. Kirchner and M. Rusinowich. Deduction with Symbolic Constraints. Revue Francaise d'Intelligence Artificielle, Volume 4:3, pp. 9–52, 1990.

    Google Scholar 

  11. D. E. Knuth and P. B. Bendix. Simple Word Problems in Universal Algebras. In J. Leech, editor, Computational Problems in Abstract Algebras, pp. 263–297. Pergammon Press, 1970.

    Google Scholar 

  12. P. Lescanne and C. Lynch, personal communication.

    Google Scholar 

  13. C. Lynch and W. Snyder. Redundancy Criteria for Constrained Completion. In Proceedings of Fifth International Conference on Rewrite Techniques and Applications, LNCS No. 690, C. Kirchner, editor, pp. 2–16, Springer-Verlag, Berlin, 1993.

    Google Scholar 

  14. Dan I. Moldovan. RUBIC: A Multiprocessor for Rule-Based Systems. IEEE Transactions on Systems, Man, and Cybernetics, 19(4):699–706, July/August 1989.

    Google Scholar 

  15. N.J. Nilsson. Problem Solving Methods in Artificial Intelligence. McGraw-Hill, New York (1971).

    Google Scholar 

  16. Tin A. Nguyen, Walton A. Perkins, Thomas J. Laffey, and Deanne Pecora. Knowledge Base Verification. AI Magazine, 8(2):69–75, Summer 1987.

    Google Scholar 

  17. Louiqa Raschid. Maintaining Consistency in a Stratified Production System Program. In AAAI90, pages 284–289, Boston, MA, July 1990.

    Google Scholar 

  18. Louiqa Raschid. A Semantics for a Class of Stratified Production System Programs. Journal of Logic Programming, 21(1):31–57, 1994.

    Article  Google Scholar 

  19. James G. Schmolze. Guaranteeing Serializable Results in Synchronous Parallel Production Systems. Journal of Parallel and Distributed Computing, 13(4):348–365, December 1991.

    Article  Google Scholar 

  20. James G. Schmolze and Wayne Snyder. Using Confluence to Control Parallel Production Systems. In Second International Workshop on Parallel Processing for Artificial Intelligence (PPAI-93), Chambery, France, August 1993. (Also to appear in Parallel Processing for Artificial Intelligence 2, Kitano, H., Suttner, C. and V. Kumar, editors, Elsevier Science Publishers B.V., 1994.)

    Google Scholar 

  21. James G. Schmolze and Wayne Snyder. Confluence and Verification for Production Rule Systems. In [1].

    Google Scholar 

  22. James G. Schmolze and Wayne Snyder. A Tool for Testing Confluence of Production Rule Systems. In [7].

    Google Scholar 

  23. James G. Schmolze and Wayne Snyder. An Operational Semantics for Production Rule Systems that Facilitates Validation and Verification. In preparation (1996).

    Google Scholar 

  24. Wayne Snyder and James G. Schmolze. A Rewriting Semantics for Production Rule Systems. Boston University Technical Report 96-001, Boston, MA (1996). (See also http://cs-www.bu.edu/faculty/snyder/pubs.html.)

    Google Scholar 

  25. M. Suwa, A. C. Scorr, and E. H. Shortliffe. An approach to verifying completeness and consistency in a rule-based expert system. AI Magazine, 3:16–21, 1982.

    Google Scholar 

  26. D. L. Waltz. Understanding Line Drawings of Scenes with Shadows. In P. Winston, editor, The Psychology of Computer Vision, pages 19–91. McGraw Hill, New York, NY., 1975.

    Google Scholar 

  27. D. Zhang and D. Nguyen. PREPARE: A Tool for Knowledge Base Verification. IEEE Transactions on Knowledge and Data Engineering, 6(6):983–989, December 1994.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

M. A. McRobbie J. K. Slaney

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Snyder, W., Schmolze, J.G. (1996). Rewrite semantics for production rule systems: Theory and applications. In: McRobbie, M.A., Slaney, J.K. (eds) Automated Deduction — Cade-13. CADE 1996. Lecture Notes in Computer Science, vol 1104. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61511-3_110

Download citation

  • DOI: https://doi.org/10.1007/3-540-61511-3_110

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61511-8

  • Online ISBN: 978-3-540-68687-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics