|Author:||Lawrence A. Bookman|
|Title:||Trajectories through Knowledge Space: A Dynamic Framework for Machine Comprehension (The Springer International Series in Engineering and Computer Science)|
|Format:||mbr docx txt mobi|
|ePUB size:||1598 kb|
|FB2 size:||1338 kb|
|DJVU size:||1818 kb|
|Publisher:||Springer; 1994 edition (September 30, 1994)|
The Springer International Series in Engineering and Computer Science. Trajectories through Knowledge Space. A Dynamic Framework for Machine Comprehension. price for USA in USD (gross). ISBN 978-1-4615-2780-0. Experiments in Acquiring Knowledge from On-line Corpora. Bookman, Lawrence A. Pages 139-163. An Analysis of the Acquired Knowledge. Pages 165-190. Bibliographic Information. The Springer International Series in Engineering and Computer Science.
Series: The Springer International Series in Engineering and Computer Science 286. File: PDF, . 9 MB. Read online. Comprehension can be viewed as a dynamic system which changes its trajectory by moving to a different point in its associational knowledge space as the input changes. At particular points in this trajectory, it is possible to extract an interpretation graph that can be used to explain some of the basic properties of the current state.
Lawrence Alan Bookman, American Computer scientist, consultant. Member Institute of Electrical and Electronics Engineers, American Association for Artificial Intelligence, Cognitive Science Society, Institute of Electrical and Electronics Engineers Computer Society. laboratories, Chelmsford, Massachusetts, 1992-1994; owner, Business Extracts International, Weston, Massachusetts, since 1995.
Trajectories Through Knowledge Space: A Dynamic Framework for Machine Comprehension. Sun Microsystems Laboratories). through the space of ASFs. That is, "energy" propa-gates through ASFs to form, over time, stable. chains corresponding to inferences. The set of ASF trajectories produced is a representation of the fine-grained knowledge that LeMICON has accumulated in working memory as a result of its processing of an input text.
The book proposes a new architecture for semantic memory, providing a framework for addressing the problem of how to represent background knowledge in a machine.
by Lawrence A. Bookman. For more help see the Common Knowledge help page.
The idea of knowledge bases lies at the heart of symbolic, or "traditional," artificial intelligence. A knowledge-based system decides how to act by running formal reasoning procedures over a body of explicitly represented knowledge-a knowledge base. Отгружается в течение 3-4 недель ( время доставки).
Lawrence A. Bookman, Trajectories Through Knowledge Space: A Dynamic Framework for Machine Comprehension. Boston, Dordrecht, London: Kluwer, 1994. 271 pp. TIMO HONKELA (a1). Helsinki University of Technology; e-mail: Timo. Published online: 01 March 1997.
Dynamic real-time scheduling algorithms are more appropriate for these systems and are used in such systems. Many of these algorithms are based on earliest deadline first (EDF) policies. The book primarily presents the algorithms and associated analysis, but guidelines, rules, and implementation considerations are also discussed, especially for the more complicated situations where mathematical analysis is difficult. In general, it is very difficult to codify and taxonomize scheduling knowledge because there are many performance metrics, task characteristics, and system configurations. Also, adding to the complexity is the fact that a variety of algorithms have been designed for different combinations of these considerations.
Trajectories through Knowledge Space: A Dynamic Framework for Machine Comprehension provides an overview of many of the main ideas of connectionism (neural. As any history student will tell you, all events must be understood within their political and sociological context. Yet science provides an interesting counterpoint to this idea, since scientific ideas stand on their own merit, and require no reference to the time and place of their conception beyond perhaps a simple citation.