"This is a popular interpretation of expert systems - computer models providing artificial intelligence to managers and businessmen - which are transforming business in the 1980s. The book gives an inside view of many international companies such as Canon, Westinghouse, IBM, Du Pont and others." New York: Times Books.

Artificial Intelligence, 35 (3). 287-310.

Artificial Intelligence, 36 (3): 401-12

IJAR, 2, 247–302.

This article describes TEAM, a transportable natural-language interface system. TEAM was constructed to test the feasibility of building a natural-language system that could be adapted to interface with new databases by users who are not experts in natural-language processing. An overview of the system design is presented, emphasizing those choices that were imposed by the demands of transportability. Several general problems of natural-language processing that were faced in constructing the system are discussed, including quantifier scoping, various pragmatic issues, and verb acquisition. TEAM is compared with several other transportable systems; this comparison includes a discussion of the range of natural language handled by each as well as a description of the approach taken to achieving transportability in each system.

In AAAI- 88, pp. 564–569.

36[#2]:177-221. Artificial Intelligence,

Citing the confusing statements in the AI literature concerning the relationship between branch and bound (B&B) and heuristic search procedures were present a simple and general formulation of B&B which should help dispel much of the confusion. We illustrate the utility of the formulation by showing that through it some apparently very different algorithms for searching And/Or trees reveal the specific nature of their similarities and differences. In addition to giving new insights into the relationships among some AI search algorithms, the general formulation also provides suggestions on how existing search procedures may be varied to obtain new algorithms.

MIT Press.

Machine Learning, 3, 9–44.