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An Interference Matching Technique for Inducing Abstractions.

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Title: An Interference Matching Technique for Inducing Abstractions.
Authors: Hayes-Roth, Frederick1, McDermott, John2, Montgomery, Christine A.
Source: Communications of the ACM. May78, Vol. 21 Issue 5, p401-411. 11p. 5 Diagrams.
Subjects: Abstract data types (Computer science), Training, Data structures, Computer programming, Algorithms
Abstract: A method for inducing knowledge by abstraction from a sequence of training examples is described. The proposed method, interference matching, induces abstractions by finding relational properties common to two or more exemplars. Three tasks solved by a program that uses an interference-matching algorithm are presented Several problems concerning the description of the training examples and the adequacy of interference matching are discussed, and directions for future research are considered. [ABSTRACT FROM AUTHOR]
Copyright of Communications of the ACM is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: A method for inducing knowledge by abstraction from a sequence of training examples is described. The proposed method, interference matching, induces abstractions by finding relational properties common to two or more exemplars. Three tasks solved by a program that uses an interference-matching algorithm are presented Several problems concerning the description of the training examples and the adequacy of interference matching are discussed, and directions for future research are considered. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Communications of the ACM is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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