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SYMBOLIC CONSTRAINTS FOR META-LOGIC PROGRAMMING.

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Title: SYMBOLIC CONSTRAINTS FOR META-LOGIC PROGRAMMING.
Authors: Christiansen, Henning, Martinenghi, Davide
Source: Applied Artificial Intelligence. Apr2000, Vol. 14 Issue 4, p345-367. 23p.
Subjects: Constraint satisfaction, Logic programming languages
Abstract: Logic programming, with its declarative bias as well as unification and the direct representation of linguistic structures, is well qualified for meta-programming, i.e., programs working with representations of other programs as their data. However, constraint techniques seem necessary in order to fully exploit this paradigm. In the DEMOII system, the language of constraint handling rules (CHRs) has been used in order to provide a functionality that appears difficult to obtain without such means. For example, reversibility of a meta-interpreter, which can be obtained by means of constraints, turns it into a powerful program generator; in the same way, negation-as-failure implemented by means of constraints provides an incremental evaluation of integrity constraints. This paper focuses on the design of such constraints and their implementation by means of CHR. [ABSTRACT FROM AUTHOR]
Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd 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.)
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  Data: Logic programming, with its declarative bias as well as unification and the direct representation of linguistic structures, is well qualified for meta-programming, i.e., programs working with representations of other programs as their data. However, constraint techniques seem necessary in order to fully exploit this paradigm. In the DEMOII system, the language of constraint handling rules (CHRs) has been used in order to provide a functionality that appears difficult to obtain without such means. For example, reversibility of a meta-interpreter, which can be obtained by means of constraints, turns it into a powerful program generator; in the same way, negation-as-failure implemented by means of constraints provides an incremental evaluation of integrity constraints. This paper focuses on the design of such constraints and their implementation by means of CHR. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd 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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