This listing is intended as an introduction to the literature on Artificial Intelligence, €”i.e., to the literature dealing with the problem of making machines behave intelligently. We have divided this area into categories and cross-indexed the references accordingly. Large bibliographies without some classification facility are next to useless. This particular field is still young, but there are already many instances in which workers have wasted much time in rediscovering (for better or for worse) schemes already reported. In the last year or two this problem has become worse, and in such a situation just about any information is better than none. This bibliography is intended to serve just that purpose-to present some information about this literature. The selection was confined mainly to publications directly concerned with construction of artificial problem-solving systems. Many peripheral areas are omitted completely or represented only by a few citations.IRE Trans. on Human Factors in Electronics, HFE-2, pages 39-55

... The literature does not include any general discussion of the outstanding problems of this field. In this article, an attempt will be made to separate out, analyze, and find the relations between some of these problems. Analysis will be supported with enough examples from the literature to serve the introductory function of a review article, but there remains much relevant work not described here.Proc. Institute of Radio Engineers 49, p. 8-30

Research report RC 294, IBM, Yorktown Heights, New York

This is an attempt to analyze attitudes and arguments brought forth by questions like "Can machines think?" and "Can machines exhibit intelligence?" Its purpose is to improve the climate which surrounds research in the field of machine or artificial intelligence. Its goal is not to convince those who answer the above questions negatively that they are wrong (although an attempt will be made to refute some of the negative arguments) but that they should be tolerant of research investigating these questions. The negative attitudes existent today tend to inhibit such research.Reprinted in Feigenbaum & Feldman, Computers and Thought (1963).Also in Datamation 9(3), March 1963, pp.34-38.Symposium on Bionics, Rand Technical Report 60 600, pp. 13-19

The studies reported here have been concerned with the programming of a digital computer to behave in a way which, if done by human beings oranimals, would be described as involving the process of learning. Whilethis is not the place to dwell on the importance of machine-learning procedures,or to discourse on the philosophical aspects,1 there is obviously avery large amount of work, now done by people, which is quite trivial inits demands on the intellect but does, nevertheless, involve some learning.Also in Computers and Thought. Feigenbaum, Edward A. and Julian Feldman (Editors) 1963.See also:IEEE XploreSome Studies in Machine Learning Using the Game of Checkers, II - Recent ProgressIBM Journal of Research and Development, 3:211-229

Teddington Conference

In Proceedings of the Symposium on Mechanisation of Thought Processes, National Physical Laboratory, Teddington, England, London: H. M. Stationary Office, pp. 3-36

The Dawn of Dynamic Programming Richard E. Bellman (1920–1984) is best known for the invention of dynamic programming in the 1950s. During his amazingly prolific career, based primarily at The University of Southern California, he published 39 books (several of which were reprinted by Dover, including Dynamic Programming, 42809-5, 2003) and 619 papers. Despite battling the crippling effects of a brain injury, he still published 100 papers during the last eleven years of his life. He was a frequent informal advisor to Dover during the 1960s and 1970s. Professor Bellman was awarded the IEEE Medal of Honor in 1979 "for contributions to decision processes and control system theory, particularly the creation and application of dynamic programming."

This is a case study in problem-solving, representing part of a program of research on complex information-processing systems. We have specifieda system for finding proofs of theorems in elementary symbolic logic, and by programming a computer to these specifications, have obtained empirical data on the problem-solving process in elementary logic. The program is called the Logic Theory Machine (LT); it was devised to learn how it is possible to solve difficult problems such as proving mathematical theorems, discovering scientific laws from data, playing chess, or understanding the meaning of English prose.The research reported here is aimed at understanding the complexp rocesses (heuristics) that are effective in problem-solving. Hence, we are not interested in methods that guarantee solutions, but which require vastamounts of computation. Rather, we wish to understand how a mathematician, for example, is able to prove a theorem even though he does not know when he starts how, or if, he is going to succeed.Proceedings of the Western Joint Computer Conference, 15:218-239. Reprinted in Feigenbaum and Feldman, Computers and Thought (1963).

Language, a journal of the Linguistic Society of America (LSA), has appeared continuously since 1925 (4 issues per year). It publishes scholarly articles that report on original research covering the field of linguistics broadly, thus treating topics that include, among others, linguistic theory (phonology, morphology, syntax, and semantics); language description; language in its social setting; the history of individual languages; language acquisition; experimentation on language perception, production, and processing; computational modeling of language; and the history of linguistics. Language also publishes research reports, discussion notes, and reviews and, beginning in 2013, has expanded to include digital content in four online-only sections: Perspectives, Phonological Analysis, Language and Public Policy, and Teaching Linguistics. Language also included the LSA Bulletin newsletter as a supplement from 1930 - 1969.