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Start Over Search Limiters Peer Reviewed Topic algorithms Topic artificial intelligence Publication Year Range More than 50 years ago
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1. Automatic Program Synthesis Reports.

Author

Ohio State Univ., Columbus. Computer and Information Science Research Center. and Biermann, A. W.

Abstract

Some of the major results of future goals of an automatic program synthesis project are described in the two papers that comprise this document. The first paper gives a detailed algorithm for synthesizing a computer program from a trace of its behavior. Since the algorithm involves a search, the length of time required to do the synthesis of nontrivial programs can be quite large. Techniques are given for preprocessing the trace information to reduce enumeration, for pruning the search using a failure memory technique, and for utilizing multiple traces to the best advantage. The results of numerous tests are given to demonstrate the value of the techniques. The other paper gives a brief overview of the automatic programming system within which the above algorithm is being used. This system is still under development. (Author/CH)

Published
1973

2. An Artificial Intelligence Approach to the Symbolic Factorization of Multivariable Polynomials. Technical Report No. CS74019-R.

Author

Virginia Polytechnic Inst. and State Univ., Blacksburg. Dept. of Computer Science. and Claybrook, Billy G.

Abstract

A new heuristic factorization scheme uses learning to improve the efficiency of determining the symbolic factorization of multivariable polynomials with interger coefficients and an arbitrary number of variables and terms. The factorization scheme makes extensive use of artificial intelligence techniques (e.g., model-building, learning, and automatic classification) in an attempt to reduce the amount of searching for the irreducible factors of the polynomial. The approach taken to polynomial factorization is quite different from previous attempts because: (1) it is distinct from numerical techniques; (2) possibilities for terms in a factor are generated from the terms in the polynomial; and (3) a reclassification technique is used to allow the application of different sets of heuristics to a polynomial during factorization attempts on it. Data presented show the importance of learning to the efficiency of operation of the scheme. Factorization times of polynomials factored by both the scheme described in this paper and Wang's implementation of Berlekamp's algorithm are given and compared, and an analysis of avariance experiment provides an indication of the significant sources of variation influencing the factorization time. (Author/DGC)

Published
1974

3. COKO III: The Cooper-Koz Chess Program.

Author

Kozdrowicki, Edward W., Cooper, Dennis W., and Lawson, C. L.

Subjects
GAMES, VIDEO games, CHESS clubs, ALGORITHMS, BOARD games, IBM computers
Abstract

COKO III is a chess player written entirely in Fortran. On the IBM 360-65, COKO III plays a minimal chess game at the rate of .2 sec cpu time per move, with a level close to lower chess club play. A selective tree searching procedure controlled by tactical chess logistics allows a deploy merit of multiple minimal game calculations to achieve some optimal move selection. The tree searching algorithms are the heart of COKO's effectiveness, yet they are conceptually simple. in addition, an interesting phenomenon called a tree searching, catastrophe has plagued COKO's entire development just as it troubles a human player. Standard exponential growth is curbed to a large extent by the definition and trimming of the Fischer set. A clear distinction between tree pruning and selective tree searching is also made. Representation of the chess environment is described along with a strategical preanalysis procedure that maps the Lasker regions. Specific chess algorithms are described which could be used as a command structure by anyone desiring to do some chess program experimentation. A comparison is made of some mysterious actions of human players and COKO III. [ABSTRACT FROM AUTHOR]

Published
1973
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4. On Shrinking Binary Picture Patterns.

Author

Levialdi, S. and Newman, W.

Subjects
PARALLEL processing, ARTIFICIAL neural networks, ALGORITHMS, COMPUTER networks, ARTIFICIAL intelligence, ELECTRONIC data processing
Abstract

A parallel processing algorithm for shrinking binary patterns to obtain single isolated elements, one for each pattern, is presented. This procedure may be used for counting patterns on a matrix, and a hardware implementation of the algorithm using large scale integrated technology is envisioned. The principal features of this method are the very small window employed (two-by-two elements), the parallel nature of the process, and the possibility of shrinking any pattern, regardless of the complexity of its configuration. Problems regarding merging and disconnection of patterns during the process as well as the determination of the maximum number of steps necessary to obtain a single isolated element from a pattern, are reviewed and discussed. An analogy with a neural network description, in terms of McCulloch-Pitts "neurons" is presented. [ABSTRACT FROM AUTHOR]

Published
1972
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