"We ask first whether we need a theory of creative thinking distinct from a theory of problem solving. Subject to minor qualifications, we conclude there is no such need -- that we call problem solving creative when the problems solved are relatively new and difficult. Next, we summarize what has been learned about problem solving by simulating certain human problem solving processes with digital computers. Finally, we indicate some of the differences in degreee that might be observed in comparing relatively creative with relative routine problem solving."RAND Corporation Paper P-1320, Santa Monica, Calif

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.

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.

See also: Princeton University Press

In Proving Theorems as Done by Man, Logician, or Machine, pp. 215-233. Proc. Summer Institute for Symbolic Logic

In Applications of Logic to Advanced Digital Computer Programming, Ann Arbor, Mich: University of Michigan Press

IRE Convention Record, pp. 56-62 (

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).

The recent work of the Unit has been primarily concerned with the employment ofthesauri in machine translation. Limited success has been achieved, in punchedcardtests, in improving the idiomatic quality and so the intelligibility of an initiallyunsatisfactory translation, by word-for-word procedures, from Italian intoEnglish, by using a program which permitted selection of final equivalents from"heads" in Roget's Thesaurus, i.e. lists of synonyms, near-synonyms and associatedwords and phrases, instead of from previously determined lists of alternativetranslations. Mechanical Translation, vol.4, nos.1 and 2, November 1957; pp. 35-43]

In Proving Theorems as Done by Man, Logician, or Machine, pp. 215-233. Proc. Summer Institute for Symbolic Logic