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Some methods of controlling the tree search in chess programs
Adelson-Velsky, G. M. | Arlazarov, V. L. | Donskoy, M. V.
Research in computer chess has been active for over three decades. Over that period, computer chess has fallen from the position of being a prominent research application in artificial intelligence to a peripheral area. In this paper, we take a retrospective look at what has been accomplished, in order to understand where the field is today and where it is headed tomorrow. Whereas the past has often been clouded by engineering passing as science, misspent effort for short-term gains, and research results with little applicability to other domains, there is evidence that computer chess is emerging from the shadow of its past and may now be recapturing some of its lost stature in the research world.
Adaptation in Natural and Artificial Systems
John Holland's pioneering book Adaptation in Natural and Artificial Systems [1975, 2nd ed. 1992] showed how the evolutionary process can be applied to solve a wide variety of problems using a highly parallel technique that is now called the genetic algorithm. The genetic algorithm transforms a population of individual objects, each with an associated fitness value, into a new generation of the population using the Darwinian principle of reproduction and survival of the fittest and naturally occurring genetic operations such as crossover (recombination) and mutation. Each individual in the population represents a possible solution to a given problem. The genetic algorithm attempts to find a very good or best solution to the problem by genetically breeding the population of individuals. In preparing to use the conventional genetic algorithm operating on fixed-length character strings to solve a problem, the user must 1. determine the representation scheme, Ann Arbor: The University of Michigan Press
Introduction to the mathematical theory of computation
"With the objective of making into a science the art of verifying computer programs (debugging), the author addresses both practical and theoretical aspects of the process. A classic of sequential program verification, this volume has been translated into almost a dozen other languages and is much in demand among graduate and advanced undergraduate computer science students. Subjects include computability (with discussions of finite automata and Turing machines); predicate calculus (basic notions, natural deduction, and the resolution method); verification of programs (both flowchart and algol-like programs); flowchart schemas (basic notions, decision problems, formalization in predicate calculus, and translation programs); and the fixpoint theory of programs (functions and functionals, recursive programs, and verification programs). The treatment is self-contained, and each chapter concludes with bibliographic remarks, references, and problems." New York: McGraw-Hill, 1974.
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.
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.