E.A. Feigenbaum and J. Feldman (Eds.). Computers and Thought. McGraw-Hill, 1963. This collection includes twenty classic papers by such pioneers as A. M. Turing and Marvin Minsky who were behind the pivotal advances in artificially simulating human thought processes with computers. All Parts are available as downloadable pdf files; most individual chapters are also available separately. COMPUTING MACHINERY AND INTELLIGENCE. A. M. Turing. CHESS-PLAYING PROGRAMS AND THE PROBLEM OF COMPLEXITY. Allen Newell, J.C. Shaw and H.A. Simon. SOME STUDIES IN MACHINE LEARNING USING THE GAME OF CHECKERS. A. L. Samuel. EMPIRICAL EXPLORATIONS WITH THE LOGIC THEORY MACHINE: A CASE STUDY IN HEURISTICS. Allen Newell J.C. Shaw and H.A. Simon. REALIZATION OF A GEOMETRY-THEOREM PROVING MACHINE. H. Gelernter. EMPIRICAL EXPLORATIONS OF THE GEOMETRY-THEOREM PROVING MACHINE. H. Gelernter, J.R. Hansen, and D. W. Loveland. SUMMARY OF A HEURISTIC LINE BALANCING PROCEDURE. Fred M. Tonge. A HEURISTIC PROGRAM THAT SOLVES SYMBOLIC INTEGRATION PROBLEMS IN FRESHMAN CALCULUS. James R. Slagle. BASEBALL: AN AUTOMATIC QUESTION ANSWERER. Green, Bert F. Jr., Alice K. Wolf, Carol Chomsky, and Kenneth Laughery. INFERENTIAL MEMORY AS THE BASIS OF MACHINES WHICH UNDERSTAND NATURAL LANGUAGE. Robert K. Lindsay. PATTERN RECOGNITION BY MACHINE. Oliver G. Selfridge and Ulric Neisser. A PATTERN-RECOGNITION PROGRAM THAT GENERATES, EVALUATES, AND ADJUSTS ITS OWN OPERATORS. Leonard Uhr and Charles Vossler. GPS, A PROGRAM THAT SIMULATES HUMAN THOUGHT. Allen Newell and H.A. Simon. THE SIMULATION OF VERBAL LEARNING BEHAVIOR. Edward A. Feigenbaum. PROGRAMMING A MODEL OF HUMAN CONCEPT FORMULATION. Earl B. Hunt and Carl I. Hovland. SIMULATION OF BEHAVIOR IN THE BINARY CHOICE EXPERIMENT Julian Feldman. A MODEL OF THE TRUST INVESTMENT PROCESS. Geoffrey P. E. Clarkson. A COMPUTER MODEL OF ELEMENTARY SOCIAL BEHAVIOR. John T. Gullahorn and Jeanne E. Gullahorn. TOWARD INTELLIGENT MACHINES. Paul Armer. STEPS TOWARD ARTIFICIAL INTELLIGENCE. Marvin Minsky. A SELECTED DESCRIPTOR-INDEXED BIBLIOGRAPHY TO THE LITERATURE ON ARTIFICIAL INTELLIGENCE. Marvin Minsky.

The investment process is a problem in decision-making under uncertainty. Our model, written as a computer program, simulates the proce- dures used in choosing investment policies for particular accounts, in evaluating the alternatives presented by the market, and in selecting the required portfolios. The analysis is based on the operations at a medium-sized national bank 1 and the decision-maker of our model is the trust imvestment officer. From A Simulation of Trust Investment, Englewood Cliffs, N.J.: Prentice-Hall, 1961.

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

This inquiry examines a technique for automatically classifying (indexing) documents according to their subject content. The task, in essence, is to have a computing machine read a document and on the basis of the occurrence of selected clue words decide to which of many subject categories the document in question belongs. This paper describes the design, execution and evaluation of a modest experimental study aimed at testing empirically one statistical technique for automatic indexing.

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

G. Selfridge was born in London 10 May PANDEMONIUM: A PARADIGM FOR LEARNING O. G. SELFRIDGE INTRODUCTION WE are proposing here a model of a process which we claim can adaptively improve itself to handle certain pattern recognition problems Which cannot be adequately specified in advance. Such problems are usual when trying' to build a machine to Imitate any one of a very large class of human data processing techniques. A speech typewriter is a good example of something that very many people have been trying unsuccessfully to build for some time. We do not suggest that we have proposed a model which can learn to typewrite from merely hearing speech. Pandemonium does not, however, seem on paper to have the same kinds of inherent restrictions or inflexibility that many previous proposals have had. The basic motif behind our model is the Inn of parallel processing. This is suggested on two grounds: first, it is often easier to handle data in a parallel manner, and, indeed, it is usually the more "natural" manner to handle it in; and, secondly, it is easier to modify an assembly of quasi We are not going to apologize for a frequent use of anthropomorphic or biamorphic terminology.

This third symbol indicates the pathological symptoms about which we have no information at all.