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
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ISTEX:AA6E9AB10754DD96B556C05DFADC6542F20740B6Le document en format XML
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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. 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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.</p></abstract><textClass><keywords scheme="Book-Subject-Collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass><keywords scheme="Book-Subject-Group"><list><label>I</label><label>I21017</label><label>I16048</label><label>I2301X</label><label>I23028</label><item><term>Computer Science</term></item><item><term>Artificial Intelligence (incl. 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In this paper we introduce a new formulation <InlineEquation ID="IEq1"><InlineMediaObject><ImageObject FileRef="978-3-540-85502-6_25_Chapter_TeX2GIFIEq1.gif" Format="GIF" Color="BlackWhite" Type="Linedraw" Rendition="HTML"></ImageObject></InlineMediaObject><EquationSource Format="TEX">$\mathcal{OP}$</EquationSource></InlineEquation>-MSDC(<Emphasis Type="Italic">q</Emphasis>) 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></InlineMediaObject><EquationSource Format="TEX">$\mathcal{OP}$</EquationSource></InlineEquation>-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></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Optimization Algorithms to Find Most Similar Deductive Consequences (MSDC)</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Optimization Algorithms to Find Most Similar Deductive Consequences (MSDC)</title></titleInfo><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><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2008</dateIssued><copyrightDate encoding="w3cdtf">2008</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><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></titleInfo><name type="personal"><namePart type="given">Klaus-Dieter</namePart><namePart type="family">Althoff</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Ralph</namePart><namePart type="family">Bergmann</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Mirjam</namePart><namePart type="family">Minor</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Alexandre</namePart><namePart type="family">Hanft</namePart><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book-series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2008</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book-Subject-Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book-Subject-Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. Robotics)</topic><topic authority="SpringerSubjectCodes" authorityURI="I16048">Mathematical Logic and Formal Languages</topic><topic authority="SpringerSubjectCodes" authorityURI="I2301X">Computer Appl. in Administrative Data Processing</topic><topic authority="SpringerSubjectCodes" authorityURI="I23028">Computer Appl. in Social and Behavioral Sciences</topic></subject><identifier type="DOI">10.1007/978-3-540-85502-6</identifier><identifier type="ISBN">978-3-540-85501-9</identifier><identifier type="eISBN">978-3-540-85502-6</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">182300</identifier><identifier type="BookID">978-3-540-85502-6</identifier><identifier type="BookChapterCount">42</identifier><identifier type="BookVolumeNumber">5239</identifier><identifier type="BookSequenceNumber">5239</identifier><identifier type="PartChapterCount">34</identifier><part><date>2008</date><detail type="part"><title>Research Papers</title></detail><detail type="volume"><number>5239</number><caption>vol.</caption></detail><extent unit="pages"><start>370</start><end>384</end></extent></part><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2008</recordOrigin></recordInfo></relatedItem><relatedItem type="series"><titleInfo><title>Lecture Notes in Computer Science</title></titleInfo><originInfo><copyrightDate encoding="w3cdtf">2008</copyrightDate><issuance>serial</issuance></originInfo><relatedItem type="constituent"><titleInfo><title>Lecture Notes in Artificial Intelligence</title></titleInfo><name type="personal"><namePart type="given">Jaime</namePart><namePart type="given">G.</namePart><namePart type="family">Carbonell</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">J\"org</namePart><namePart type="family">Siekmann</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Klaus-Dieter</namePart><namePart type="family">Althoff</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Ralph</namePart><namePart type="family">Bergmann</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Mirjam</namePart><namePart type="family">Minor</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Alexandre</namePart><namePart type="family">Hanft</namePart><role><roleTerm type="text">editor</roleTerm></role></name><genre type="sub-series"></genre><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SubSeriesID">1244</identifier></relatedItem><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SeriesID">558</identifier><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2008</recordOrigin></recordInfo></relatedItem><identifier type="istex">AA6E9AB10754DD96B556C05DFADC6542F20740B6</identifier><identifier type="DOI">10.1007/978-3-540-85502-6_25</identifier><identifier type="ChapterID">25</identifier><identifier type="ChapterID">Chap25</identifier><accessCondition type="use and reproduction" contentType="copyright">Springer-Verlag Berlin Heidelberg, 2008</accessCondition><recordInfo><recordContentSource>SPRINGER</recordContentSource><recordOrigin>Springer-Verlag Berlin Heidelberg, 2008</recordOrigin></recordInfo></mods></metadata></istex></record>Pour manipuler ce document sous Unix (Dilib)
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