"The LISP language is designed primarily for symbolic data processing. It has been used for symbolic calculations in differential and integral calculus, electrical circuit theory, mathematical logic, game playing, and other fields of artificial intelligence.LISP is a formal mathematical language. It is therefore podsible to give a concise yet complete description of it. Such is the purpose of this first section of the manual. Other sections will describe ways of using LISP to advantage and will explain extensions of the language which make it a convenient programming system."The M.I.T. Press

"This report describes some experiments in constructing a compiler that makes use of heuristic problem~solving techniques such as those incorporated in the General Problem Solver (GPS). The experiments were aimed at the dual objectives of throwing light on some of the problems of constructing more powerful programming languages and compilers, and of testing whether the task of writing a computer program can be regarded as a "problem" in the sense in which that term is used in GPS. The present paper is concerned primarily with the second objective--with analyzing some of the problem-solving processes that are involved in writing computer programs. At the present stage of their development, no claims will be made for the heuristic programming procedures described here as practical approaches to the construction of compilers. Their interest lies in what they teach us about the nature of the programming task." JACM, 10, 493-506. See also: Artificial intelligence and self-organizing systems: Experiments with a Heuristic Compiler. (http://dl.acm.org/citation.cfm?id=806076)

Article based on Ph.D. dissertation at Carnegie Tech. "... the problem of meaning is of major importance in the study of the nature of intelligence, and that a useful definition of meaning must include not only denotation but connotation and implication as well. To handle these important questions it is necessary to study cognitive organizations which are more complex than those upon which most psychological theories are based. A central question is the storage of large numbers of interrelated propositions in a manner which efficiently uses memory capacity." In E.A. Feigenbaum & J. Feldman (Eds.) Computers and Thought, pp. 217-233. McGraw-Hill, 1963.

Technical Report, Panoramic Research, Inc, Palo Alto, CA

Englewood Cliffs, N.J.: Prentice Hall

In Self-Organizing Systems 1962, pp 435–461.

A model of human information processing during concept formation has been constructed, using a list processing, digital computer program. The program's input consists of descriptions of objects in terms of dimensions and values. The universe of objects is divided into two or more sets. The program attempts to form a decision rule, based upon the descriptions of the objects, which can be used to assign any previously presented or new object to its correct set. The program is a model for human information processing, rather than an artificial intelligence system.

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The programming of a proof procedure is discussed in connection with trial runs and possible improvements. In [1] is set forth an algorithm for proving theorems of quantification theory which is an improvement in certain respects over previously available algorithms such as that of [2]. The present paper deals with the programming of the algorithm of [1] for the New York University, Institute of Mathematical Sciences' IBM 704 computer, with some modifications in the algorithm suggested by this work, with the results obtained using the completed algorithm. Familiarity with [1] is assumed throughout. The algorithm of [1] consists of two interlocking parts.

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