This paper presents a framework for characterizing automatic programming systems in terms of how a task is communicated to the system, the method and time at which the system acquires the knowledge to perform the task, and the characteristics of the resulting program to perform that task. It describes one approach In which both tasks and knowledge about the task domain are stated in natural language In the terms of that domain. All knowledge of computer science necessary to implement the task is internalized inside the system.In IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 20-23 August 1973, Stanford University Stanford, California, pp.494-499

A new signature table technique is described together with an improved book learning procedure which is thought to be much superior to the linear polynomial method described earlier. Full use is made of the so called “alpha-beta” pruning and several forms of forward pruning to restrict the spread of the move tree and to permit the program to look ahead to a much greater depth than it other- wise could do. While still unable to outplay checker masters, the program’s playing ability has been greatly improved.See also:IEEE XploreAnnual Review in Automatic Programming, Volume 6, Part 1, 1969, Pages 1–36Some Studies in Machine Learning Using the Game of CheckersIBM J of Research and Development ll, No.6, 1967,601

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.

Proceedings of the Symposium on the Mechanisation of Thought Processes.Teddington, Middlesex, England: National Physical Laboratory.