Metalevel architectures are gaining widespread use in many domains. This paper examines the metalevel as a system level, describes the type of knowledge embodied in a metalevel, and discusses the characteristics of good metalevel representations.


Knowledge Level Concise Statement Object Level Symbol Level Weak Precondition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Bell C., Adey S., T. Urwin, Jones G., Simpson R. and Sadri F., Report of the Short Session on Planning and Control, Report of Fourth Planning SIG Workshop. Alvey Programme IKBS Research Theme, Alvey Directorate, London, England, 1985.Google Scholar
  2. Benjamin D. P., Using a Metatheory as a Functional Representation, International journal of Intelligent Systems, volume 3 (3), Fall 1988.Google Scholar
  3. Benjamin D. P., A Metatheory for Reasoning about Preconditions, TR-87–055, Philips Laboratories, 1987.Google Scholar
  4. Benjamin D. P., Learning Strategies by Reasoning about Rules, 10th International Joint Conference on Artificial Intelligence; Milano, Italy, August 1987.Google Scholar
  5. Bosman A., Decision Support Systems, Corporate Models and the Handling of Organisations, INFORMATIE23,11 (1981), pp. 681–92.Google Scholar
  6. Bundy A. and Welham B., Using Meta-Level Inference for Selective Application of Multiple Rewrite Rule Sets in Algebraic Manipulation, Artificial Intelligence 16 (1981), pp. 189–212.MathSciNetCrossRefGoogle Scholar
  7. Bundy A., Meta-level Inference and Consciousness, in The Mind and the Machine, S. Torrance (editor), Horwood, 1984.Google Scholar
  8. Dietterich T., Learning at the Knowledge Level, Machine Learning 1 (1986), pp. 287–316.Google Scholar
  9. Kedar-Cabelli S. and McCarty L. T., Explanation-Based Generalization as Resolution TfaeoremProving,Proc.4thInternationalWorkshoponMachineLearning,Morgm Kaufmann, Los Altos, CA, 1987, pp. 383–389.Google Scholar
  10. Levesque H., A Functional Approach to Knowledge Representation, Artificial Intelligence 23 (1984), pp. 155–212.MATHCrossRefGoogle Scholar
  11. Lowry M. R., Algorithm Synthesis Through Problem Reformulation, Ph. D. Dissertation, Stanford University, 1987.Google Scholar
  12. McClintock C. G., The Metatheorectical Bases of Social Psychological Theory, Behav. Sci. 30,3 (1985), pp. 155–73.CrossRefGoogle Scholar
  13. Michalski R. S., A Theory and Methodology of Inductive Learning, Artificial Intelligence 20 (February 1983), pp. 111–161.MathSciNetCrossRefGoogle Scholar
  14. Morris E. K., Higgins S. T. and Bickel W. K., Comments on cognitive science in the experimental analysis of behavior, Behavior Analyst 5 (2) (1982), pp. 109–125.Google Scholar
  15. Newell A., The Knowledge Level, Artificial Intelligence 18,2 (1982), pp. 87–127.CrossRefGoogle Scholar
  16. Perrault C. R., On the Mathematical Properties of Linguistic Theories, Comput. Linguist. 10, 3–4 (1984), pp. 165–76.Google Scholar
  17. Pylyshyn Z. W., Computation and Cognition, MIT Press, Cambridge, MA, 1984.Google Scholar
  18. Silver B., Metalevel Inference, Elsevier Science, Amsterdam, Netherlands, 1986.Google Scholar
  19. Stanoulov N., An Evolutionary Approach in Information Systems Science, J. Am. Soc. Inf. Sci. 33, 5 (1982), pp. 311–16.CrossRefGoogle Scholar
  20. Teske J. A. and Pea R. D., Metatheoretical Issues in Cognitive Science., Journal of Mind & Behavior 2 (2) (1981), pp. 123–178.Google Scholar
  21. Thompson T. F. and Wojcik R. M.,MELD: An Implementation of aMeta-Level Architecture forPwcessDiagnosis,ProceedingsoftheFirstConferenceonArtificialIntelligence Applications, December 1984, pp. 321–330.Google Scholar

Copyright information

© Kluwer Academic Publishers 1990

Authors and Affiliations

  • D. Paul Benjamin
    • 1
  1. 1.Philips LaboratoriesNorth American Philips CorporationBriarcliff ManorUSA

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