At the recent AAAI conference at Stanford, it became apparent that many new AI research centers are being established around the country in industrial and governmental settings and in universities that have not paid much attention to AI in the past. At the same time, many of the established AI centers are in the process of converting from older facilities, primarily based on Decsystem-10 and Decsystem-20 machines, to a variety of newer options. At present, unfortunately, there is no simple answer to the question of what machines, operating systems, and languages a new or upgrading AI facility should use, and this situation has led to a great deal of confusion and anxiety on the part of those researchers and administrators who are faced with making this choice. In this article I will survey the major alternatives available at present and those that are clearly visible on the horizon, and I will try to indicate the advantages and disadvantages of each for AI work. This is mostly information that we have gathered at CMU in the course of planning for our own future computing needs, but the opinions expressed are my own.

This overview takes a general look at search in problem solving, indicating some connections with topics considered in other Handbook chapters. The these general ideas are found in programs for natural second section considers algorithms that use these language understanding, information retrieval, automatic representations. In methods, which use information about the nature and this chapter of the Handbook we examine search as a tool structure of the problem domain to limit the search. Most of the Finally, the chapter reviews several well-known early examples considered are problems that are relatively easy programs based on search, together with some related to formalize. The first of these is a may be, however, that the description of a task-domain database, which describes both the current task-domain situation is too large for multiple versions to be stored situation and the goal.

A four-volume collection of articles on all the major topics of AI at that time, with an extensive bibliography. Vol I (Avron Barr and Edward A. Feigenbaum, 1981) (https://books.google.com/books?isbn=1483214370). Vol II (Avron Barr, Edward A. Feigenbaum, Paul R. Cohen, 1982) (https://books.google.com/books?isbn=1483214389). Vol III (Paul R. Cohen and Edward A. Feigenbaum, 1982) (https://books.google.com/books?isbn=1483214397). Vol IV (Avron Barr and Paul R. Cohen, 1989) (https://books.google.com/books?isbn=1483214370). Reading, Mass.: Addison-Wesley.

This is an excerpt from the Handbook of Artificial Intelligence, a compendium of hundreds of articles about AI ideas, techniques, and programs being prepared at Stanford University by AI researchers and students from across the country. In addition to articles describing the specifics of various AI programming methods, the Handbook contains dozens of overview articles like this one, which attempt to give historical and scientific perspective to work in the different areas of AI research. This article is from the Handbook chapter on natural language understanding. Cross-references to other articles in the handbook have been removed-terms discussed in more detail elsewhere are italicized. Many people have contributed to this chapter, including especially Anne Gardner, James Davidson, and Terry Winograd. Avron Barr and Edward A. Feigenbaum are the Handbook's general editors.

Two premises, reflected in the title, underlie the perspective from which I will consider research in natural language processing in this article. First, progress on building computer systems that process natural languages in any meaningful sense (i.e., systems that interact reasonably with people in natural language) requires considering language as part of a larger communicative situation. Second, as the phrase “utterance and objective” suggests, regarding language as communication requires consideration of what is said literally, what is intended, and the relationship between the two.

The Heuristic Programming Project of the Stanford University Computer Science Department is a laboratory of about fifty people whose main goals are to model the nature of scientific reasoning processes in various types of scientific problems and various areas of science and medicine; and to construct expert systems — programs that achieve high levels of performance on tasks that normally require significant human expertise for their solution.

"In the fall of 1978 we decided to produce a special issue of the SIGART Newsletter devoted to a survey of current knowledge representation research. We felt that there were twe useful functions such an issue could serve. First, we hoped to elicit a clear picture of how people working in this subdiscipline understand knowledge representation research, to illuminate the issues on which current research is focused, and to catalogue what approaches and techniques are currently being developed. Second -- and this is why we envisaged the issue as a survey of many different groups and projects -- we wanted to provide a document that would enable the reader to acquire at least an approximate sense of how each of the many different research endeavours around the world fit into the field as a whole. It would of course be impossible to produce a final or definitive document accomplishing these goals: rather, we hoped that this survey could initiate a continuing dialogue on issues in representation, a project for which this newsletter seems the ideal forum. It has been many months since our original decision was made, but we are finally able to present the results of that survey. Perhaps more than anything else, it has emerged as a testament to an astounding range and variety of opinions held by many different people in many different places. The following few pages are intended as an introduction to the survey as a whole, and to this issue of the newsletter. We will briefly summarize the form that the survey took, discuss the strategies we followed in analyzing and tabulating responses, briefly review the overall sense we received from the answers that were submitted, and discuss various criticisms which were submitted along with the responses. The remainder of the volume has been designed to be roughly self-explanatory at each point, so that one may dip into it at different places at will. Certain conventions, however, particularly regarding indexing and tabulating, will also be explained in the remainder of this introduction." ACM SIGART Newsletter No. 70.

American Journal of Computational Linguistics, Volume 6, Number 2, April-June 1980

ACM Computing Survey 12:213-253

Proc. Canadian Soc. for Computational Studies of Intelligence, Univ. Victoria, Victoria, BC (1980)