Associative Networks, representation and use of knowledge by computers

Findler, Nicholas V.

ISBN 10: 0122563808 / ISBN 13: 9780122563805

Published by Academic Press, 1979

About this Item

Hardback, fading, light shelf wear to exterior; otherwise very good condition with clean text, tight binding. No dust jacket.

Seller Inventory # 94509

Bibliographic Details

• Title: Associative Networks, representation and use of knowledge by computers
• Publisher: Academic Press
• Publication Date: 1979
• Language: English
• ISBN 10: 0122563808
• ISBN 13: 9780122563805
• Binding: Hardcover
• Condition: Good

About this title

Synopsis

Associative Representation and Use of Knowledge by Computers is a collection of papers that deals with knowledge base of programs exhibiting some operational aspects of understanding. One paper reviews network formalism that utilizes unobstructed semantics, independent of the domain to which it is applied, that is also capable of handling significant epistemological relationships of concept structuring, attribute/value inheritance, multiple descriptions. Another paper explains network notations that encode taxonomic information; general statements involving quantification; information about processes and procedures; the delineation of local contexts, as well as the relationships between syntactic units and their interpretations. One paper shows that networks can be designed to be intuitively and formally interpretable. Network formalisms are computer-oriented logics which become distinctly significant when access paths from concepts to propositions are built into them. One feature of a topical network organization is its potential for learning. If one topic is too large, it could be broken down where groupings of propositions under the split topics are then based on "co-usage" statistics. As an example, one paper cites the University of Maryland artificial intelligence (AI) group which investigates the control and interaction of a meaning-based parser. The group also analyzes the inferences and predictions from a number of levels based on mundane inferences of actions and causes that can be used in AI. The collection can be useful for computer engineers, computer programmers, mathematicians, and researchers who are working on artificial intelligence.