This paper presents two new machine learning procedures used to arrive at “knowledgeable” static evaluators for checker board positions. The static evaluators are compared with each other, and with the linear polynomial used by Samuel [9], using two different numerical indices reflecting the extent to which they agree with the choices of checker experts in the course of tabulated book games. The new static evaluators are found to perform about equally well, despite the relative simplicity of the second; and they perform noticably better than the linear polynomial. An indication of the significance of the absolute values of these two numerical indices is provided by a discussion of a simple, purely heuristic, static evaluator, whose performance indices lie between those of the polynomial and those of the other two static evaluators.

British Medical Journal, 1, 376–380.

Rather than provide a general review of Dreyfus critique this article concentrates on certain fundamental criticisms that Dreyfus directs at the information-processing approach to cognitive psychology and points out the unique conception of what it means to understand cognition which separates a phenomenologist from the typical cognitive psychologist.

Perceptual structures and semantic relations; Processes of learning and comprehension; Subjective probability distributions for imperfectly known quantities; Theory of rule induction: knowledge acquired in concept learning, serial pattern learning and problem solving; Problem solving and rule induction: a unified view; Quote the raven?

If natural language processing systems are ever to achieve natural, cooperative behavior, they must be able to process input that is ill-formed lexically, syntactically, semantically, or pragmatically. Systems must be able to partially understand, or at least give specific, appropriate error messages, when input does not correspond to their model of language and of context.We propose meta-rules and a control structure under which they are invoked as a framework for processing ill-formed input. The left-hand side of a meta-rule diagnoses a problem as a violated rule of normal processing. The right-hand side relaxes the violated rule and states how processing may be resumed, if at all.Examples discussed in the paper include violated grammatical tests, omitted articles, homonyms, spelling/typographical errors, unknown words, violated selection restrictions, personification, and metonymy. An implementation of a meta-rule processor within the framework of an augmented transition network parser is also described.

This is a partial theory of thinking, combining a number of classical and modern concepts from psychology, linguistics, and AI. Whenever one encounters a new situation (or makes a substantial change in one's viewpoint) he selects from memory a structure called a frame, a remembered framework to be adopted to fit reality by changing details as necessary. A frame is a data-structure for representing a stereotyped situation, like being in a certain kind of living room, or going to a child's birthday party. Attached to each frame are several kinds of information. Some of this information is about how to use the frame.

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Edinburgh: Edinburgh University Press

NATO Advanced Study Institute an Computer Oriented Learning Processes, p. 15 pgs.

See also Werbos, Paul J. (1994). The Roots of Backpropagation. From Ordered Derivatives to Neural Networks and Political Forecasting. New York, NY: John Wiley & Sons, Inc. Rumelhart, David E.; Hinton, Geoffrey E., Williams, Ronald J. (8 October 1986). "Learning representations by back-propagating errors". Nature323 (6088): 533–536. doi:10.1038/323533a0.Ph.D. thesis, Harvard University.