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Why Should Machines Learn?
See also: C.I.P. #425, Departments of Computer Science and Psychology, Carnegie-Mellon University, 1980In Michalski, R. S., Carbonell, J. G., and Mitchell, T. M. (Eds), Machine Learning, An Artificial Intelligence Approach, Tioga Press, Palo Alto, CA
Semantical Considerations on Nonmonotonic Logic
In Proceedings of the Eighth IJCAI, pages 272-279. IJCAI, Karlsruhe, West Germany, August, 1983. Volume 1.
A deductive model of belief
Proceedings of the Eighth International Joint Conference on Artificial Intelligence, Karlsruhe, West Germany, 377-381
Building Expert Systems
Hayes-Roth, F., Waterman, D. A., Lenat, D. B.
Reading, Mass.: Addison-Wesley.
An Overview of Meta-Level Architecture
Genesereth, M. R. | Smith, D. E.
"One of the biggest problems in AT programming is the difficulty of specifying control. Meta-level architecture is a knowledge engineering approach to coping with this difficulty. The key feature of the architecture is a declarative control language that allows one to write partial specifications of program behavior. This flexibility facilitates incremental system dcvclopment and the integration of disparate architectures like demons, object-oriented programming, and controlled deduction. This paper presents the language, describes an appropriate, and cliscusses the issues of compiling. It illustrales the architecture with a variety of examples and reports some experience in using the architecture in building expert systems."Earlier: M. Genesereth and D.E. Smith. Meta-level Architecture. Memo HPP-81-6, Computer Science Department, Stanford University, 1981.In Proceedings of the AAAI, Washington, DC., August, 1983
Classification in the KL-ONE representation system
Schmolze, J. G. | Lipkis, T. A.
In IJCAI-83, pp. 330–332.
Metaphor Interpretation as Selective Inferencing: Cognitive Processes in Understanding Metaphor
Empirical Studies in the Arts, vol. 1, No. 1, pp. 17-34, and vol. 1, No. 2, pp. 125-142.
An Approach to Verifying Completeness and Consistency in a Rule-Based Expert System
Suwa, Motoi, Scott, A. Carlisle, Shortliffe, Edward H.
We describe a program for verifying that a set of rules in an expert system comprehensively spans the knowledge of a specialized domain. The program has been devised and tested within the context of the ONCOCIN System, a rule-based consultant for clinical oncology. The stylized format of ONCOIN's rule has allowed the automatic detection of a number of common errors as the knowledge base has been developed. This capability suggests a general mechanism for correcting many problems with knowledge base completeness and consistency before they can cause performance errors.