Technology
A comparison and evaluation of three machine learning procedures as applied to the game of checkers
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.
Minds, machines and phenomenology: Some reflections on Dreyfusâ What Computers Canât Do
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.
Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences
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.
Problem solving and rule induction: A unified view
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?
Natural language understanding systems within the AI paradigm: A survey and some comparisons
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.
Semantics and speech understanding
In researc which lan uac; assumed knowled way it use of provide impreci recent h into a is to e. In that on re of th is used the cons s, to na se acous years, utomati (r,et a nost e need e lan u (pragma traints ke sens tic sit there has c speech u computer of this s to pro are (its s tics). It and expec e of the i nal that i been a nderstan to und recent a vide th yntax an will th tations nherentl s human rroat increase in dine, the purpose of erstand the spoken ctivity, it has been e computer with a d semantics) and the en be able to make which this knowledfre y vaf ue, sloppy and soeech. Syntactic constraints and expectations are based on the patterns formed by a Riven set of linguistic objects, e. .
A Framework for Representing Knowledge
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.
Search Strategies for the Task of Organic Chemical Synthesis
A computer program has been written that successfully discovers syntheses for complex organic chemical moleculeB. The definition of the search space and strategies for heuristic search are described in this paper. It is not growing like a tree... ...In small proportions we just beauties see; - Ben Jonson. Introduction The design of application of artificial intelligence to a scientific task such as Organic Chemical Synthesis was the topic of a Doctoral Thesis completed in the summer of 197I. Chemical synthesis in practice involves i) the choice of molecule to be synthesized; ii) the formulation and specification of a plan for synthesis (involving a valid reaction pathway leading from commercial or readily available compounds to the target compounds with consideration of feasibility regarding the purposes of synthesis); iii) the selection of specific individual steps of reaction and their temporal ordering for execution; iv) the experimental execution of the synthesis and v) the redesign of syntheses, if necessary, depending upon the experimental results. In contrast to the physical synthesis of the molecule, the activity in ii) above can be termed the'formal synthesis'. This development of the specification of syntheses involves no laboratory technique and is carried out mainly on paper and in the minds of chemists (and now within a computer's memory!). Importance and Difficulty of Chemical Synthesis The importance of chemical synthesis is undeniable and there is emphatic testimony to the high regard held by scientists for synthesis chemists.