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2017 | OriginalPaper | Buchkapitel

Combining Answer Set Programming with Description Logics for Analogical Reasoning Under an Agent’s Preferences

verfasst von : Teeradaj Racharak, Satoshi Tojo, Nguyen Duy Hung, Prachya Boonkwan

Erschienen in: Advances in Artificial Intelligence: From Theory to Practice

Verlag: Springer International Publishing

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Abstract

Analogical reasoning makes use of a kind of resemblance of one thing to another for assigning properties from one context to another. This kind of reasoning is used quite often by human beings, especially in unseen situations. The key idea of analogy is to identify a good similarity; however, similarity may be varied on subjective factors (i.e. an agent’s preferences). This paper studies an implementation of this phenomena using an answer set programming with Description Logics. The main idea underlying the proposed approach lies in the so-called Argument from Analogy developed by Walton [1]. Finally, the paper relates the approach to others and discusses future directions.

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Fußnoten
1
In the original definition of preference profile [12, 13], both \(\mathfrak {i} ^\mathfrak {c} \) and \(\mathfrak {i} ^\mathfrak {r} \) are mapped to \(\mathbb {R}_{\ge 0}\) which is a minor error.
 
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Metadaten
Titel
Combining Answer Set Programming with Description Logics for Analogical Reasoning Under an Agent’s Preferences
verfasst von
Teeradaj Racharak
Satoshi Tojo
Nguyen Duy Hung
Prachya Boonkwan
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-60045-1_33

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