Artificial Intelligence, vol. 9, pp. 287-306.

Lingua, vol. 44, pp. 311-338. Also in Readings in Natural Language Processing, B. Grosz, K. Sparck-Jones, and B. Webber, editors, pp. 339-352, Morgan Kaufmann Publishers, Los Altos, California.

Retrospective on lessons learned from the Dendral project."The DENDRAL and Meta-DENDRAL programs are products of a large, interdisciplinary group of Stanford University scientists concerned with many and highly varied aspects of the mechanization of scientific reasoning and the formalization of scientific knowledge for this purpose. An early motivation for our wok was to explore the power of existing Al methods, such as heuristic search, for reasoning in difficult scientific problems. Another concern has been to exploit the AI methodology to understand better some fundamental questions in the philosophy of science, for example the processes by which explanatory hypotheses are discovered or judged adequate. From the start, the project has had an applications dimension. It has sought to develop "expert level" agents to assist in the solution of problems in their discipline that require complex symbolic reasoning. The applications dimension is the focus of this paper."Artificial Intelligence 11 (1-2): 5-24

In Gallaire, H. and Minker, J. (Eds.), Logic and Data Bases, pp. 293-322. Plenum.

Computer J. 21:149-153

Xerox PARC, Palo Alto, Calif.

"The terms adaptation, learning, concept-formation, induction, self-organization, and self-repair have all been used in the context of learning system (LS) research. The research has been conducted within many different scientific communities, however, and these terms have come to have a variety of meanings. It is therefore often difficult to recognize that problems which are described differently may in fact be identical. Learning system models as well are often tuned to the require- ments of a particular discipline and are not suitable for application in related disciplines."In Encyclopedia of Computer Science and Technology, Vol. 11. Dekker

GUS is the first of a series of experimental computer systems that we intend to construct as part of a program of research on language understanding. In large measure, these systems will fill the role of periodic progress reports, summarizing what we have learned, assessing the mutual coherence of the various lines of investigation we have been following, and suggesting where more emphasis is needed in future work. GUS (Genial Understander System) is intended to engage a sympathetic and highly cooperative human in an English dialog, directed towards a specific goal within a very restricted domain of discourse. As a starting point, GUS was restricted to the role of a travel agent in a conversation with a client who wants to make a simple return trip to a single city in California. There is good reason for restricting the domain of discourse for a computer system which is to engage in an English dialog.

This paper discusses an effort in the application of artificial intelligence to the access of data from a large, distributed data base over a computer network. A running system is described that provides real-time access over the ARPANET to a data base distributed over several machines. The system accepts a rather wide range of natural language questions about the data, plans a sequence of appropriate queries to the data base management system to answer the question, determines on which machine(s) to carry out the queries, establishes links to those machines over the ARPANET, monitors the prosecution of the queries and recovers from certain errors in execution, and prepares a relevant answer. In addition to the components that make up the demonstration system, more sophisticated functionally equivalent components are discussed and proposed. The work described in this paper represents the joint efforts of an integrated, energetic group at SRI. Members of this group include Rich Fikes (now at Xerox PARC), Koichi Furukawa (now at ETL).

IJCAI 1977, 389-397