Constraint Programming for Context Comprehension

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Abstract

A close similarity is demonstrated between context comprehension, such as discourse analysis, and constraint programming. The constraint store takes the role of a growing knowledge base learned throughout the discourse, and a suitable con- straint solver does the job of incorporating new pieces of knowledge. The language of Constraint Handling Rules, CHR, is suggested for defining constraint solvers that reflect “world knowledge” for the given domain, and driver algorithms may be ex- pressed in Prolog or additional rules of CHR. It is argued that this way of doing context comprehension is an instance of abductive reasoning. The approach fits with possible worlds semantics that allows both standard first-order and non-monotonic semantics.
Original languageEnglish
Title of host publicationContext in Computing : A Cross-Disciplinary Approach for Modeling the Real World
EditorsPatrick Brézillon, Avelino J. Gonzalez
PublisherSpringer Science+Business Media
Publication date2014
Pages401-418
Chapter25
ISBN (Print)978-1-4939-1886-7
ISBN (Electronic)978-1-4939-1887-4
DOIs
Publication statusPublished - 2014

Cite this

Christiansen, H. (2014). Constraint Programming for Context Comprehension. In P. Brézillon, & A. J. Gonzalez (Eds.), Context in Computing: A Cross-Disciplinary Approach for Modeling the Real World (pp. 401-418). Springer Science+Business Media. https://doi.org/10.1007/978-1-4939-1887-4_25