President Dwight D. Eisenhower, in 1959, presented him with five hundred dollars, awarded by Recording for the Blind Inc., for outstanding work as a blind student ^{[4]}.

Why don't we have artificial intelligence yet? There were some remarkable achievements in very early days of artificial intelligence. This is my favorite one, a young student who happened to be blind named James Slagle wrote a Ph.D. thesis in 1961 that was nearly as good as a good MIT freshman at doing intergroup calculus. Up until then there was no general theory of how to integrate functions. Isaac Newton invented the process but could not solve it. People like Gauss and others spent the next couple of centuries on it. By 1950, there was a great collection called the Bateman Manuscript Project run by the American Mathematical Society and they collected integrals.

Theorem-Proving

Abstract of Experiments With a Multipurpose, Theorem-Proving Heuristic Program. ^{[8]} from the ACM Portal:

The heuristic program discussed searches for a constructive proof or disproof of a given proposition. It uses a search procedure which efficiently selects the seemingly best proposition to work on next. This program is multipurpose in that the domains it can handle are varied. As an initial experiment, the program was given the task of searching for proofs and disproofs of propositions about Kalah end games. Kalah is a two-person game. In another experiment the program, after some modifications, played the game of Kalah. This program was compared with another tree-searching procedure, the Alpha-Beta minimax procedure; the results have been encouraging since the program is fast and efficient. Its greatest usefulness is in solving large problems. It is hoped that this program has added one more step toward the goal of eventually obtaining computer programs which can solve intellectually difficult problems.

M & N procedure

Abstract of Experiments with the M & N Tree-Searching Program^{[9]} from the ACM Portal:

The M & N procedure is an improvement to the mini-max backing-up procedure widely used in computer programs for game-playing and other purposes. It is based on the principle that it is desirable to have many options when making decisions in the face of uncertainty. The mini-max procedure assigns to a MAX (MIN) node the value of the highest (lowest) valued successor to that node. The M & N procedure assigns to a MAX (MIN) node some function of the M (N) highest (lowest) valued successors. An M & N procedure was written in LISP to play the game of Kalah, and it was demonstrated that the M & N procedure is significantly superior to the mini-max procedure. The statistical significance of important conclusions is given. Since information on statistical significance has often been lacking in papers on computer experiments in the artificial intelligence field, these experiments can perhaps serve as a model for future work.

James R. Slagle (1961). A Heuristic Program that Solves Symbolic Integration Problems in Freshman Calculus, Symbolic Automatic Integrator (Saint). pdf

James R. Slagle (1963). A Heuristic Program that Solves Symbolic Integration Problems in Freshman Calculus. Journal of the ACM, Vol. 10, No. 4

James R. Slagle (1963). Game Trees, M & N Minimaxing, and the M & N alpha-beta procedure. Artificial Intelligence Group Report 3, UCRL-4671, University of California

James R. Slagle (1964). An Efficient Algorithm for Finding Certain Minimum-Cost Procedures for Making Binary Decisions. Journal of the ACM, Vol. 11, No. 3

Erach A. Irani, John P. Matts, John M. Long, James R. Slagle, POSCH group (1989). Using Artificial Neural Nets for Statistical Discovery: Observations after Using Backpropogation, Expert Systems, and Multiple-Linear Regression on Clinical Trial Data. University of Minnesota, Minneapolis, MN 55455, USA, Complex Systems 3, pdf

Home * People * James R. SlagleJames Robert Slagle, (born March 1, 1934)an American mathematician, computer scientist, and since 1984 Distinguished Professor of Computer Science at the University of Minnesota, Minneapolis, with former appointments at Johns Hopkins University, National Institutes of Health, Bethesda, Maryland, Naval Research Laboratory, Lawrence Radiation Laboratory, University of California and Massachusetts Institute of Technology. As Freshman Calculus Student and Ph.D. candidate at MIT, supervised by Marvin Minsky

^{[1]}in 1961, he wrote his dissertation entitledHeuristic Program that Solves Symbolic Integration Problems in Freshman Calculus, Symbolic Automatic Integrator (Saint)^{[2]}, which is acknowledged as first Expert system^{[3]}. His further research interests covers heuristic Theorem-Proving and as application heuristic search.President Dwight D. Eisenhower, in 1959, presented him with five hundred dollars, awarded by Recording for the Blind Inc., for outstanding work as a blind student

^{[4]}.^{[5]}## Table of Contents

## Saint

Quote by Marvin Minsky on Slagle's, discussing his bookSymbolicAutomaticIntegratorThe Emotion Machine^{[6]}^{[7]}:Why don't we have artificial intelligence yet? There were some remarkable achievements in very early days of artificial intelligence. This is my favorite one, a young student who happened to be blind named James Slagle wrote a Ph.D. thesis in 1961 that was nearly as good as a good MIT freshman at doing intergroup calculus. Up until then there was no general theory of how to integrate functions. Isaac Newton invented the process but could not solve it. People like Gauss and others spent the next couple of centuries on it. By 1950, there was a great collection called the Bateman Manuscript Project run by the American Mathematical Society and they collected integrals.## Theorem-Proving

Abstract ofExperiments With a Multipurpose, Theorem-Proving Heuristic Program.^{[8]}from the ACM Portal:The heuristic program discussed searches for a constructive proof or disproof of a given proposition. It uses a search procedure which efficiently selects the seemingly best proposition to work on next. This program is multipurpose in that the domains it can handle are varied. As an initial experiment, the program was given the task of searching for proofs and disproofs of propositions about Kalah end games. Kalah is a two-person game. In another experiment the program, after some modifications, played the game of Kalah. This program was compared with another tree-searching procedure, the Alpha-Beta minimax procedure; the results have been encouraging since the program is fast and efficient. Its greatest usefulness is in solving large problems. It is hoped that this program has added one more step toward the goal of eventually obtaining computer programs which can solve intellectually difficult problems.## M & N procedure

Abstract ofExperiments with the M & N Tree-Searching Program^{[9]}from the ACM Portal:The M & N procedure is an improvement to the mini-max backing-up procedure widely used in computer programs for game-playing and other purposes. It is based on the principle that it is desirable to have many options when making decisions in the face of uncertainty. The mini-max procedure assigns to a MAX (MIN) node the value of the highest (lowest) valued successor to that node. The M & N procedure assigns to a MAX (MIN) node some function of the M (N) highest (lowest) valued successors. An M & N procedure was written in LISP to play the game of Kalah, and it was demonstrated that the M & N procedure is significantly superior to the mini-max procedure. The statistical significance of important conclusions is given. Since information on statistical significance has often been lacking in papers on computer experiments in the artificial intelligence field, these experiments can perhaps serve as a model for future work.## Selected Publications

^{[10]}^{[11]}^{[12]}^{[13]}## 1959

1959).Formal integration on a digital computer. 14th national meeting of the Association for Computing Machinery## 1960 ...

1961).A Heuristic Program that Solves Symbolic Integration Problems in Freshman Calculus, Symbolic Automatic Integrator (Saint). pdf1963).A Heuristic Program that Solves Symbolic Integration Problems in Freshman Calculus. Journal of the ACM, Vol. 10, No. 41963).Game Trees, M & N Minimaxing, and the M & N alpha-beta procedure.Artificial Intelligence Group Report 3, UCRL-4671, University of California1964).An Efficient Algorithm for Finding Certain Minimum-Cost Procedures for Making Binary Decisions. Journal of the ACM, Vol. 11, No. 31964).On an algorithm for minimum-cost procedures. Communications of the ACM, Vol. 7, No. 111965).A multipurpose Theorem Proving Heuristic Program that learns. IFIP Congress 65, Vol. 21965).Experiments with a deductive question-answering program. Communications of the ACM, Vol. 8, No. 121967).Automatic Theorem Proving With Renamable and Semantic Resolution. Journal of the ACM, Vol. 14, No. 41968).Experiments With a Multipurpose, Theorem-Proving Heuristic Program. Journal of the ACM, Vol. 15, No. 1^{[14]}1969).Experiments With Some Programs That Search Game Trees. Journal of the ACM, Vol. 16, No. 2, pdf, pdf1969).Completeness Theorems for Semantic Resolution In Consequence-Finding. IJCAI-69, pdf## 1970 ...

1970).A New Algorithm for Generating Prime Implicants. IEEE Transactions on Computers, Vol. 19, No. 41970).Experiments with the M & N Tree-Searching Program. Communications of the ACM, Vol. 13, No. 31971).Artificial Intelligence: The Heuristic Programming Approach. McGraw-Hill, New York. amazon1971).Experiments in automatic learning for a multipurpose hueristic program. Communications of the ACM, Vol. 14, No. 21971).Application of game tree searching techniques to sequential pattern recognition. Communications of the ACM, Vol. 14, No. 21979).Using Rewriting Rules for Connection Graphs to Prove Theorems. Artificial Intelligence## 1980 ...

1980).Finding a good figure that approximately passes through given points. Pattern Recognition, 19801980).The Prospect of an Under Water Naval Robot. Naval Engineers Journal, Vol. 92, No. 11981).MARK I Robot. IJCAI'1981, pdf1984).Freedom descriptions: A way to find figures that approximate given points. Pattern Recognition, 19841986).Heterogeneous discrete expenditure for diminishing returns. Naval Research Logistics Quarterly, Vol. 33, No. 21989).Using Artificial Neural Nets for Statistical Discovery: Observations after Using Backpropogation, Expert Systems, and Multiple-Linear Regression on Clinical Trial Data. University of Minnesota, Minneapolis, MN 55455, USA, Complex Systems 3, pdf## 1990 ...

1990).Formulating an approach to develop a system for the temporal analysis of clinical trial data: The POSCH AI project. Annals of Mathematics and Artificial Intelligence, Volume 2, Numbers 1-41994).Ideas for Intelligent User Interface Design.## 2000 ...

2000).An Integrated Connectionist Approach to Reinforcement Learning for Robotic Control. ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning## External Links

## References

1968).Experiments With a Multipurpose, Theorem-Proving Heuristic Program. Journal of the ACM, Vol. 15, No. 11970).Experiments with the M & N Tree-Searching Program. Communications of the ACM, Vol. 13, No. 3## What links here?

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