In IJCAI-83, pp. 330–332.

CACM, 26 (11), 832-43

In Michalski, R. S., Carbonell, J. G., and Mitchell, T. M. (Eds.), Machine Learning: An Artificial Intelligence Approach, pp. 163–190. Morgan Kaufmann.

A theory of analogy must describe how the meaning of an analogy is derived from the meanings of its parts. In the structure-mapping theory, the interpretation rules are characterized as implicit rules for mapping knowledge about a base domain into a target domain. Two important features of the theory are (a) the rules depend only on syntactic properties of the knowledge representation, and not on the specific content of the domains; and (b) the theoretical framework allows analogies to be distinguished cleanly from literal similarity statements, applications of abstractions, and other kinds of comparisons. Two mapping principles are described: (a) Relations between objects, rather than attributes of objects, are mapped from base to target; and (b) The particular relations mapped are determined by systematicity, as defined by the existence of higher-order relations. Cognitive Science, 7 (2), 155-€“170.

Artificial Intelligence, 21 (3), 327-50

Empirical Studies in the Arts, vol. 1, No. 1, pp. 17-34, and vol. 1, No. 2, pp. 125-142.

Swartout accrue from such an endeavor?

Journal of Pragmatics Volume 1, Issue 1, April 1977, pp. 69-83. Reprinted in Sedlow, Walter A., et al. (eds.) Computers in Language Research 2: Berlin: Degruiter & Co, 1983,

Interactive intelligent systems often suffer from a basic conflict between their computationally intensive nature and the need for responsiveness to a user. This paper introduces the Interviewer/Reasoner model, which helps to reduce this conflict. The Interviewer's primary function is to gather data while providing an acceptable response time to the user. The Reasoner does most of the symbolic computation for the system.

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.