Specifying Meta-Level Architectures for Rule-Based Systems

  • Michael Beetz
Conference paper
Part of the Informatik-Fachberichte book series (INFORMATIK, volume 152)

Abstract

Explicit and declarative representation of control knowledge and well-structured rule bases are crucial requirements for efficiently developing and maintaining rule-based systems. The CATWEAZLE rule interpreter allows the knowledge engineer to partition rule bases and specify meta-level architectures for control.

Among others the following questions arise immediately when one wants to provide tools for specifying meta-level architectures for control:

  1. 1.

    What is a suitable language to specify meta-level architectures for control?

     
  2. 2.

    How can general and declarative languages for meta-level architectures be interpreted efficiently?

     

This paper outlines solutions to both research questions provided by the CATWEAZLE rule interpreter:

  1. 1.

    CATWEAZLE provides a small set of concepts based on a separation of control knowledge in control strategies and control tactics and a further categorization of control strategies.

     
  2. 2.

    For rule-based systems it is efficient to extend the RETE pattern matching algorithm such that it can process control knowledge as well.

     

Keywords

Cond Mellon 

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References

  1. [Aiello,Levi-84]
    L. Aiello and G. Levi:The Uses of Metaknowledge in AI Systems,Proc. of Sixth European Conference on Artificial Intelligence, ECAI-84, Pisa, September 1984, pp. 707 - 717.Google Scholar
  2. [Beetz-87a]
    M. Beetz:A Knowledge Representation Language for Control Knowledge in Rule-Based Systems Proc. of Expert Systems’87, Concepts and Tools,Nuremberg, April 1987.Google Scholar
  3. [Beetz-87b]
    M. Beetz:Specifying Meta-Level Architectures for Rule-Based Systems,Diploma Thesis,University of Kaiserslautern, 1987, also: SEKI-REPORT (forthcoming) University of Kaiserslautern, 1987.Google Scholar
  4. [Bundy,Welham-81]
    A. Bundy, B. Welham:Using Meta-Level Inference for Selective Application of Multiple Rewrite Rules in Algebraic Manipulation,Artificial Intelligence, Vol. 16, No. 2, pp. 189 - 212, 1981.MathSciNetGoogle Scholar
  5. [Clancey-83a]
    W. Clancey:The Advantages of Abstract Control Knowledge in Expert System Design, Proc. of the National Conference on Artificial Intelligence, AAAI-83, pp. 74-78Google Scholar
  6. [Davis-80]
    R. Davis Meta-Rules: Reasoning about Control,Artificial Intelligence, Vol. 15 (1980), pp. 179 - 222Google Scholar
  7. [Forgy-79]
    C. Forgy:On the Efficient Implementation of Production Systems, Ph.D. Dissertation,Computer Science Department, Carnegie Mellon University, Pittsburgh, 1979Google Scholar
  8. [Forgy-81]
    C. Forgy:OPS5 User Manual,CMU-CS-81-135,Computer Science Department, Carnegie Mellon University, Pittsburgh, 198Google Scholar
  9. [Genesereth-83a]
    M. Genesereth:An Overview of Meta-Level Architecture,Proc. of the National Conference on Artificial Intelligence, AAAI-83, pp. 119-124.Google Scholar
  10. [Hayes-73]
    P. Hayes:Computation and Deduction,Proceedings of Mathematical Foundations of Computer Science (MFCS) Symposium, Czechoslovakian Academy of Sciences.Google Scholar
  11. [Hayes-77]
    P. Hayes:In Defence of Logic,Proceedings of the Fifth International Joint Conference on Artificial Intelligence, IJCAI-77,Cambridge, Mass., pp. 559-565.Google Scholar
  12. [Hayes-79]
    P. Hayes:The Logic of Frames,in: Frame Conceptions and Text Understanding, Walter de Gruyter and Co., pp. 46-61.Google Scholar
  13. [Hayes-Roth-85]
    F.Hayes-Roth:Rule-Based Systems,Communications of ACM,Vol. 28(1985), No. 9, pp. 921-932Google Scholar
  14. [Martins-84]
    G. Martins:The Overselling of Expert Systems,DATAMATION, Vol. 30 No. 18, 1984, pp. 76 - 80Google Scholar
  15. [Neches, Swartout,Moore-85]
    R. Neches, W.R. Swartout, J. Moore:Explainable (and Maintainable) Expert Systems,Proceedings of the Ninth ernational Joint Conference on Artificial Intelligence, IJCAI-85,pg. 383-389.Google Scholar
  16. [Puppe-83]
    F. Puppe:MED1 - Ein heuristisches Diagnosesystem mit effizienter Kontrollstruktur, MEMO SEKI-83-04,Universitaet Kaiserslautern, 1983.Google Scholar
  17. [Stefik,etal.-82]
    M. Stefik, J. Aikins, J. Benoit, L. Birnbaum, F. Hayes-Roth, E. Sacerdoti: The Organization of Expert Systems - A Tutorial,Artificial Intelligence 18 (1982), pp. 135 - 173.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1987

Authors and Affiliations

  • Michael Beetz
    • 1
  1. 1.Basisentwicklung (EVX)TA Triumph-Adler AGNuernberg 80Germany

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