From Natural to Artificial Intelligence: Numerical processing for cognitive tasks
Identifieur interne : 002463 ( Hal/Curation ); précédent : 002462; suivant : 002464From Natural to Artificial Intelligence: Numerical processing for cognitive tasks
Auteurs : Frédéric Alexandre [France] ; Hervé Frezza-Buet [France]Source :
Descripteurs français
- mix :
- Wicri :
- topic : économie.
Abstract
Cognitive modeling can be derived from expert knowledge integration or experimental data analysis. This chapter introduces these two main ways of modeling and concentrates on the latter aspect and the related numerical tools together with their possible role. More precisely, the evolutionary approach is presented for optimization and tuning, artificial neural networks for associative reasoning and the stochastic approach for planning. We then conclude on the need for a modular framework, integrating these numerical and other symbolic tools.
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<front><div type="abstract" xml:lang="en">Cognitive modeling can be derived from expert knowledge integration or experimental data analysis. This chapter introduces these two main ways of modeling and concentrates on the latter aspect and the related numerical tools together with their possible role. More precisely, the evolutionary approach is presented for optimization and tuning, artificial neural networks for associative reasoning and the stochastic approach for planning. We then conclude on the need for a modular framework, integrating these numerical and other symbolic tools.</div>
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<idno type="halRefHtml">Paul Bourgine and Jean-Pierre Nadal. Cognitive Economics, Springer, pp.113-130, 2004</idno>
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<idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">From Natural to Artificial Intelligence: Numerical processing for cognitive tasks</title>
<author role="aut"><persName><forename type="first">Frédéric</forename>
<surname>Alexandre</surname>
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<email>Frederic.Alexandre@inria.fr</email>
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<title level="m">Cognitive Economics</title>
<editor>Paul Bourgine and Jean-Pierre Nadal</editor>
<imprint><publisher>Springer</publisher>
<biblScope unit="pp">113-130</biblScope>
<date type="datePub">2004</date>
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<textClass><keywords scheme="author"><term xml:lang="fr">reinforcement</term>
<term xml:lang="fr">cognition</term>
<term xml:lang="fr">economics</term>
<term xml:lang="fr">artificial neural networks</term>
<term xml:lang="fr">evolutionary approach</term>
<term xml:lang="fr">économie</term>
<term xml:lang="fr">réseaux de neurones artificiels</term>
<term xml:lang="fr">approche évolutionnaire</term>
<term xml:lang="fr">renforcement</term>
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<abstract xml:lang="en">Cognitive modeling can be derived from expert knowledge integration or experimental data analysis. This chapter introduces these two main ways of modeling and concentrates on the latter aspect and the related numerical tools together with their possible role. More precisely, the evolutionary approach is presented for optimization and tuning, artificial neural networks for associative reasoning and the stochastic approach for planning. We then conclude on the need for a modular framework, integrating these numerical and other symbolic tools.</abstract>
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