A model for generalization based on confirmatory induction
Identifieur interne : 003129 ( Istex/Corpus ); précédent : 003128; suivant : 003130A model for generalization based on confirmatory induction
Auteurs : Nicolas Lachiche ; Pierre MarquisSource :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: Confirmatory induction is based on the assumption that unknown individuals are similar to known ones, i.e. they satisfy the properties shared by known individuals. This assumption can be represented inside a non-monotonic logical framework. Accordingly, existing approaches to confirmatory induction take advantage of the machinery developed so far for non-monotonic inference. However, they are based on completion policies that are unnecessary strong for the induction purpose. The contribution of this paper is twofold: some basic requirements that any model for generalization based on confirmatory induction should satisfy are proposed. Then, a model for generalization based on Hempel's notion of confirmation is introduced. This model is rational in the sense that it satisfies the rationality postulates we exhibit; moreover, the completion principle on which this model is based captures exactly the similarity assumption, hence the model can be considered minimal as well.
Url:
DOI: 10.1007/3-540-62858-4_80
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<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">A model for generalization based on confirmatory induction</title><author><name sortKey="Lachiche, Nicolas" sort="Lachiche, Nicolas" uniqKey="Lachiche N" first="Nicolas" last="Lachiche">Nicolas Lachiche</name><affiliation><mods:affiliation>Campus scientifique, CRIN/CNRS - INRIA Lorraine, Bâtiment LORIA, B.P. 239, 54506, Vandœzuvre-lès-Nancy Cedex, France</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: lachiche@loria.fr</mods:affiliation></affiliation></author><author><name sortKey="Marquis, Pierre" sort="Marquis, Pierre" uniqKey="Marquis P" first="Pierre" last="Marquis">Pierre Marquis</name><affiliation><mods:affiliation>CRIL/Université d'Artois, Rue de l'Université, S.P. 16, 62307, Lens Cedex, France</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: marquis@lens.lifl.fr</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:CF8DA7BA0D6989BB6E0BD4E57BF247E6641ADFCB</idno><date when="1997" year="1997">1997</date><idno type="doi">10.1007/3-540-62858-4_80</idno><idno type="url">https://api.istex.fr/ark:/67375/HCB-682XM91Z-R/fulltext.pdf</idno><idno type="wicri:Area/Istex/Corpus">003129</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">003129</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">A model for generalization based on confirmatory induction</title><author><name sortKey="Lachiche, Nicolas" sort="Lachiche, Nicolas" uniqKey="Lachiche N" first="Nicolas" last="Lachiche">Nicolas Lachiche</name><affiliation><mods:affiliation>Campus scientifique, CRIN/CNRS - INRIA Lorraine, Bâtiment LORIA, B.P. 239, 54506, Vandœzuvre-lès-Nancy Cedex, France</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: lachiche@loria.fr</mods:affiliation></affiliation></author><author><name sortKey="Marquis, Pierre" sort="Marquis, Pierre" uniqKey="Marquis P" first="Pierre" last="Marquis">Pierre Marquis</name><affiliation><mods:affiliation>CRIL/Université d'Artois, Rue de l'Université, S.P. 16, 62307, Lens Cedex, France</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: marquis@lens.lifl.fr</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="s" type="main" xml:lang="en">Lecture Notes in Computer Science</title><title level="s" type="abbrev">Lect Notes Comput Sci</title><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: Confirmatory induction is based on the assumption that unknown individuals are similar to known ones, i.e. they satisfy the properties shared by known individuals. This assumption can be represented inside a non-monotonic logical framework. Accordingly, existing approaches to confirmatory induction take advantage of the machinery developed so far for non-monotonic inference. However, they are based on completion policies that are unnecessary strong for the induction purpose. The contribution of this paper is twofold: some basic requirements that any model for generalization based on confirmatory induction should satisfy are proposed. Then, a model for generalization based on Hempel's notion of confirmation is introduced. This model is rational in the sense that it satisfies the rationality postulates we exhibit; moreover, the completion principle on which this model is based captures exactly the similarity assumption, hence the model can be considered minimal as well.</div></front></TEI><istex><corpusName>springer-ebooks</corpusName><author><json:item><name>Nicolas Lachiche</name><affiliations><json:string>Campus scientifique, CRIN/CNRS - INRIA Lorraine, Bâtiment LORIA, B.P. 239, 54506, Vandœzuvre-lès-Nancy Cedex, France</json:string><json:string>E-mail: lachiche@loria.fr</json:string></affiliations></json:item><json:item><name>Pierre Marquis</name><affiliations><json:string>CRIL/Université d'Artois, Rue de l'Université, S.P. 16, 62307, Lens Cedex, France</json:string><json:string>E-mail: marquis@lens.lifl.fr</json:string></affiliations></json:item></author><arkIstex>ark:/67375/HCB-682XM91Z-R</arkIstex><language><json:string>eng</json:string></language><originalGenre><json:string>ReviewPaper</json:string></originalGenre><abstract>Abstract: Confirmatory induction is based on the assumption that unknown individuals are similar to known ones, i.e. they satisfy the properties shared by known individuals. This assumption can be represented inside a non-monotonic logical framework. Accordingly, existing approaches to confirmatory induction take advantage of the machinery developed so far for non-monotonic inference. However, they are based on completion policies that are unnecessary strong for the induction purpose. The contribution of this paper is twofold: some basic requirements that any model for generalization based on confirmatory induction should satisfy are proposed. Then, a model for generalization based on Hempel's notion of confirmation is introduced. 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This assumption can be represented inside a non-monotonic logical framework. Accordingly, existing approaches to confirmatory induction take advantage of the machinery developed so far for non-monotonic inference. However, they are based on completion policies that are unnecessary strong for the induction purpose. The contribution of this paper is twofold: some basic requirements that any model for generalization based on confirmatory induction should satisfy are proposed. Then, a model for generalization based on Hempel's notion of confirmation is introduced. This model is rational in the sense that it satisfies the rationality postulates we exhibit; moreover, the completion principle on which this model is based captures exactly the similarity assumption, hence the model can be considered minimal as well.</p></abstract><textClass ana="subject"><keywords scheme="book-subject-collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass ana="subject"><keywords scheme="book-subject"><list><label>I</label><item><term type="Primary">Computer Science</term></item><label>I21017</label><item><term type="Secondary" subtype="priority-1">Artificial Intelligence (incl. 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This assumption can be represented inside a non-monotonic logical framework. Accordingly, existing approaches to confirmatory induction take advantage of the machinery developed so far for non-monotonic inference. However, they are based on completion policies that are unnecessary strong for the induction purpose. The contribution of this paper is twofold: some basic requirements that any model for generalization based on confirmatory induction should satisfy are proposed. Then, a model for generalization based on Hempel's notion of confirmation is introduced. This model is rational in the sense that it satisfies the rationality postulates we exhibit; moreover, the completion principle on which this model is based captures exactly the similarity assumption, hence the model can be considered minimal as well.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>A model for generalization based on confirmatory induction</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>A model for generalization based on confirmatory induction</title></titleInfo><name type="personal"><namePart type="given">Nicolas</namePart><namePart type="family">Lachiche</namePart><affiliation>Campus scientifique, CRIN/CNRS - INRIA Lorraine, Bâtiment LORIA, B.P. 239, 54506, Vandœzuvre-lès-Nancy Cedex, France</affiliation><affiliation>E-mail: lachiche@loria.fr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Pierre</namePart><namePart type="family">Marquis</namePart><affiliation>CRIL/Université d'Artois, Rue de l'Université, S.P. 16, 62307, Lens Cedex, France</affiliation><affiliation>E-mail: marquis@lens.lifl.fr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre displayLabel="ReviewPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" type="conference" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-BFHXPBJJ-3">conference</genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">1997</dateIssued><copyrightDate encoding="w3cdtf">1997</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: Confirmatory induction is based on the assumption that unknown individuals are similar to known ones, i.e. they satisfy the properties shared by known individuals. This assumption can be represented inside a non-monotonic logical framework. Accordingly, existing approaches to confirmatory induction take advantage of the machinery developed so far for non-monotonic inference. However, they are based on completion policies that are unnecessary strong for the induction purpose. The contribution of this paper is twofold: some basic requirements that any model for generalization based on confirmatory induction should satisfy are proposed. Then, a model for generalization based on Hempel's notion of confirmation is introduced. This model is rational in the sense that it satisfies the rationality postulates we exhibit; moreover, the completion principle on which this model is based captures exactly the similarity assumption, hence the model can be considered minimal as well.</abstract><relatedItem type="host"><titleInfo><title>Machine Learning: ECML-97</title><subTitle>9th European Conference on Machine Learning Prague, Czech Republic, April 23–25, 1997 Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Maarten</namePart><namePart type="family">van Someren</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Gerhard</namePart><namePart type="family">Widmer</namePart><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book-series" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0G6R5W5T-Z">book-series</genre><originInfo><publisher>Springer</publisher><copyrightDate encoding="w3cdtf">1997</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="I16021">Algorithm Analysis and Problem Complexity</topic></subject><identifier type="DOI">10.1007/3-540-62858-4</identifier><identifier type="ISBN">978-3-540-62858-3</identifier><identifier type="eISBN">978-3-540-68708-5</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">46768</identifier><identifier type="BookID">3540628584</identifier><identifier type="BookChapterCount">33</identifier><identifier type="BookVolumeNumber">1224</identifier><part><date>1997</date><detail type="volume"><number>1224</number><caption>vol.</caption></detail><extent unit="pages"><start>154</start><end>161</end></extent></part><recordInfo><recordOrigin>Springer-Verlag, 1997</recordOrigin></recordInfo></relatedItem><relatedItem type="series"><titleInfo><title>Lecture Notes in Computer Science</title><subTitle>Lecture Notes in Artificial Intelligence</subTitle></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">Jörg</namePart><namePart type="family">Siekmann</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">G.</namePart><namePart type="family">Goos</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">J.</namePart><namePart type="family">Hartmanis</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">J.</namePart><namePart type="family">van Leeuwen</namePart><role><roleTerm type="text">editor</roleTerm></role></name><originInfo><publisher>Springer</publisher><copyrightDate encoding="w3cdtf">1997</copyrightDate><issuance>serial</issuance></originInfo><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SeriesID">558</identifier><recordInfo><recordOrigin>Springer-Verlag, 1997</recordOrigin></recordInfo></relatedItem><identifier type="istex">CF8DA7BA0D6989BB6E0BD4E57BF247E6641ADFCB</identifier><identifier type="ark">ark:/67375/HCB-682XM91Z-R</identifier><identifier type="DOI">10.1007/3-540-62858-4_80</identifier><identifier type="ChapterID">14</identifier><identifier type="ChapterID">Chap14</identifier><accessCondition type="use and reproduction" contentType="copyright">Springer-Verlag, 1997</accessCondition><recordInfo><recordContentSource authority="ISTEX" authorityURI="https://loaded-corpus.data.istex.fr" valueURI="https://loaded-corpus.data.istex.fr/ark:/67375/XBH-RLRX46XW-4">springer</recordContentSource><recordOrigin>Springer-Verlag, 1997</recordOrigin></recordInfo></mods><json:item><extension>json</extension><original>false</original><mimetype>application/json</mimetype><uri>https://api.istex.fr/ark:/67375/HCB-682XM91Z-R/record.json</uri></json:item></metadata></istex></record>Pour manipuler ce document sous Unix (Dilib)
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