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Natural language input for a computer problem solving system

Classics

'might do even better to make people change to some mor- "intelligent" language. We thus define "understazding" in terms of statements in English The Should the computer store 2he information contained in these statements? SAD SAM program written'by Robert Lindsay at Carnegie Tech in 1960. Mary?" or "Who are Jack's grandchildren?" SAD SAH extracts the meaning "Mary, Tom's sister, went to the meรฉting," "The sum of two numbers is 96, anรฉ one of the numbers is 16 "One of the numbers is 56" I chose this problem coaaext for a number of reasons"?irst


A Deductive Question-Answering System

Classics

Reprinted in Marvin Minsky (ed), Semantic Information Processing, pp. 354-402, Cambridge, MA: MIT Press, 1968.


Indexing and dependency logic for answering English questions

Classics

This paper describes a computer system which uses a combination of coordinate indexing and structure matching techniques to extract from English questions many criteria which can be used for selecting and recognizing answers. A complete index of all content words in text is first searched to find information-rich statements which may be answers to the question. Each of these statements is then dependency analyzed to determine if the words (or synonyms) which correspond to question words maintain the dependency relations holding in the question. A simple semantic evaluation of structurally acceptable answers follows. A human editor working with the computer system helps to resolve syntactic ambiguities which are otherwise a major stumbling block in question-answering systems.


STeLLA: A Scheme for a Learning Machine

Classics

Electrical and Computer Engineering will give you the power to change the world. From providing clean, efficient energy to controlling digital data, from global communication to nanotechnologies, from robotics to entertainment, the future is being created by our graduates today. If you want to make a difference, study Electrical and Electronic Engineering or Computer Engineering at UC - the future is in your hands.



Computers and Thought

Classics

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.


A program for parsing sentences and making inferences about kinship relations

Classics

In A. C. Hoggatt and F. E. Balderston (Eds.), Symposium on simulation models: Methodology and applications to the behavioral sciences. Cincinnati: South-Western Publishing, 111-138.


Experiments on the Mechanisation of Game Learning: 1

Classics

This paper describes a trial-and-error device which learns to play the game of Noughts and Crosses. It was initially constructed from matchboxes and coloured beads and subsequently simulated in essentials by a program for a Pegasus 2 computer. The parameters governing the adaptive behaviour of this automaton are described and preliminary observations on its performance are briefly reported.


A Heuristic Program that Solves Symbolic Integration Problems in Freshman Calculus

Classics

A large high-speed general-purpose digital computer (IBM 7090) wasProgrammed to solve elementary symbolic integration problems at approximatelythe level of a good college freshman. The program is called SAINT,an acronym for "Symbolic Automatic INTegrator." The SAINT programis written in LISP (McCarthy, 1960), and most of the work reported hereJs the substance of a doctoral dissertation at the Massachusetts Institute ofTechnology (Slagle, 1961). This discussion concerns the SAINT programand its performance.Some typical samples of SAINT's external behavior are given so thatthe reader may think in concrete terms. Journal of the ACM, Vol 10, No. 4, pp. 507-520, October 1963.


Syntactic Analysis of English by Computer: A Survey

Classics

A statement in a spoken language may be regarded as a one-dimensional string of symbols used to communicate an idea from the speaker to a listener. The dimensionality of the statement is limited by the need for presenting words in a single time sequence. However, evidence indicates that most information and ideas are not stored by people in one-dimensional arrays isomorphic to these linear strings. This implies that a speaker must use certain complex information manipulating processes to transform the stored information to a linear output string, and that a listener, in order to "understand" the speaker, must use another set of processes to decode this linear string. In order for communication to take place, the information map of both the listener and the speaker must be approximately the same, at least for the universe of discourse.