Optimization Algorithms to Find Most Similar Deductive Consequences (MSDC)
Identifieur interne : 001826 ( Istex/Corpus ); précédent : 001825; suivant : 001827Optimization Algorithms to Find Most Similar Deductive Consequences (MSDC)
Auteurs : Babak MougouieSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2008.
Abstract
Abstract: Finding most similar deductive consequences, MSDC, is a new approach which builds a unified framework to integrate similarity-based and deductive reasoning. In this paper we introduce a new formulation $\mathcal{OP}$ -MSDC(q) of MSDC which is a mixed integer optimization problem. Although mixed integer optimization problems are exponentially solvable in general, our experimental results show that $\mathcal{OP}$ -MSDC(q) is surprisingly solved faster than previous heuristic algorithms. Based on this observation we expand our approach and propose optimization algorithms to find the k most similar deductive consequences k-MSDC.
Url:
DOI: 10.1007/978-3-540-85502-6_25
Links to Exploration step
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<front><div type="abstract" xml:lang="en">Abstract: Finding most similar deductive consequences, MSDC, is a new approach which builds a unified framework to integrate similarity-based and deductive reasoning. In this paper we introduce a new formulation $\mathcal{OP}$ -MSDC(q) of MSDC which is a mixed integer optimization problem. Although mixed integer optimization problems are exponentially solvable in general, our experimental results show that $\mathcal{OP}$ -MSDC(q) is surprisingly solved faster than previous heuristic algorithms. Based on this observation we expand our approach and propose optimization algorithms to find the k most similar deductive consequences k-MSDC.</div>
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) of MSDC which is a mixed integer optimization problem. Although mixed integer optimization problems are exponentially solvable in general, our experimental results show that <InlineEquation ID="IEq2"><InlineMediaObject><ImageObject FileRef="978-3-540-85502-6_25_Chapter_TeX2GIFIEq2.gif" Format="GIF" Color="BlackWhite" Type="Linedraw" Rendition="HTML"></ImageObject>
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<EquationSource Format="TEX">$\mathcal{OP}$</EquationSource>
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-MSDC(<Emphasis Type="Italic">q</Emphasis>
) is surprisingly solved faster than previous heuristic algorithms. Based on this observation we expand our approach and propose optimization algorithms to find the <Emphasis Type="Italic">k</Emphasis>
most similar deductive consequences <Emphasis Type="Italic">k</Emphasis>
-MSDC.</Para>
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<name type="personal"><namePart type="given">Babak</namePart>
<namePart type="family">Mougouie</namePart>
<affiliation>Department of Business Information Systems II, University of Trier, Trier, Germany</affiliation>
<affiliation>DFKI GmbH, Knowledge Management Department, Kaiserslautern, Germany</affiliation>
<affiliation>E-mail: mougouie@dfki.de</affiliation>
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<abstract lang="en">Abstract: Finding most similar deductive consequences, MSDC, is a new approach which builds a unified framework to integrate similarity-based and deductive reasoning. In this paper we introduce a new formulation $\mathcal{OP}$ -MSDC(q) of MSDC which is a mixed integer optimization problem. Although mixed integer optimization problems are exponentially solvable in general, our experimental results show that $\mathcal{OP}$ -MSDC(q) is surprisingly solved faster than previous heuristic algorithms. Based on this observation we expand our approach and propose optimization algorithms to find the k most similar deductive consequences k-MSDC.</abstract>
<relatedItem type="host"><titleInfo><title>Advances in Case-Based Reasoning</title>
<subTitle>9th European Conference, ECCBR 2008, Trier, Germany, September 1-4, 2008. Proceedings</subTitle>
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<name type="personal"><namePart type="given">Klaus-Dieter</namePart>
<namePart type="family">Althoff</namePart>
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</name>
<name type="personal"><namePart type="given">Ralph</namePart>
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