Technology
Intelligent behavior in problem solving machines
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Automatic programming-properties and performance of FORTRAN systems I and II
From the The Teddington Conference– D. V. Blake and A. M. Uttley (Eds.). Proceedings of the Symposium on Mechanisation of Thought Processes, National Physical Laboratory, Teddington, Middlesex, England, London: H. M. Stationary Office.
Computer vs chess player
Paul G. Allen was an investor and philanthropist. He created and advanced world-class projects and high-impact initiatives that changed and improved the way people live, learn, work, and experience the world through arts, education, entertainment, sports, business, and technology. He cofounded Microsoft with Bill Gates in 1975 and remained the company's chief technologist until he left Microsoft in 1983. Allen founded Vulcan Inc. in 1986 and the Experience Music Project, now the Museum of Pop Culture, in 2000. With lifetime giving of about $1 billion, Allen was named one of the top philanthropists in America.
Maximum likelihood estimation from incomplete data
Biometrics is a scientific journal emphasizing the role of statistics and mathematics in the biological sciences. Its object is to promote and extend the use of mathematical and statistical methods in pure and applied biological sciences by describing developments in these methods and their applications in a form readily assimilable by experimental scientists. JSTOR provides a digital archive of the print version of Biometrics. Authorized users may be able to access the full text articles at this site.
Automatic programming-properties and performance of FORTRAN systems I and II
Proceedings of the Symposium on the Mechanisation of Thought Processes.Teddington, Middlesex, England: National Physical Laboratory.
The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain
Psychological Review, Vol. 65, pp. 386 ff.
Empirical Explorations with the Logic Theory Machine: A Case Study in Heuristics
This is a case study in problem-solving, representing part of a program of research on complex information-processing systems. We have specifieda system for finding proofs of theorems in elementary symbolic logic, and by programming a computer to these specifications, have obtained empirical data on the problem-solving process in elementary logic. The program is called the Logic Theory Machine (LT); it was devised to learn how it is possible to solve difficult problems such as proving mathematical theorems, discovering scientific laws from data, playing chess, or understanding the meaning of English prose.The research reported here is aimed at understanding the complexp rocesses (heuristics) that are effective in problem-solving. Hence, we are not interested in methods that guarantee solutions, but which require vastamounts of computation. Rather, we wish to understand how a mathematician, for example, is able to prove a theorem even though he does not know when he starts how, or if, he is going to succeed.Proceedings of the Western Joint Computer Conference, 15:218-239. Reprinted in Feigenbaum and Feldman, Computers and Thought (1963).