Emotional processes modelling in decision making
Identifieur interne : 000350 ( Hal/Checkpoint ); précédent : 000349; suivant : 000351Emotional processes modelling in decision making
Auteurs : Karim Mahboub [France]Source :
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Abstract
Emotion is inseparable from cognitive processes and therefore plays a major role in decision making. As a result, it is becoming increasingly important in today's scientific research. The aim of this thesis is to show the advantages of an emotional approach, and to prove that in certain cases computer models equipped with artificial emotions prove to be more efficient than their purely cognitive equivalents. Based on this observation, two emotional models were realised from different study perspectives. They underline the impact of the addition of an emotional dimension in the elaboration of a fast, adaptive and efficient decision. The first developed model uses a graph for strategies representation in order to solve a ten-year-old pupil mathematics exercise called the Cascades problem. Emotion is represented there as weighting values in the graph edges dynamically managed by an ant algorithm. The tests carried out on two versions, one emotional and the other one fully cognitive, show that the use of an emotional model produces a more efficient and adaptive solving. In addition, a second model named GAEA aims at simulating a robot equipped with sensors and effectors and thrown into a prey-predators environment inside which it must survive. Its behaviour is determined by its internal program that evolves thanks to a linear genetic program algorithm manipulating a population of program individuals. Results are promising and indicate that the population produces individuals whose behaviour is more and more adapted, and whose internal activity is analogous to the emergence of relevant emotional reactions.
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As a result, it is becoming increasingly important in today's scientific research. The aim of this thesis is to show the advantages of an emotional approach, and to prove that in certain cases computer models equipped with artificial emotions prove to be more efficient than their purely cognitive equivalents. Based on this observation, two emotional models were realised from different study perspectives. They underline the impact of the addition of an emotional dimension in the elaboration of a fast, adaptive and efficient decision. The first developed model uses a graph for strategies representation in order to solve a ten-year-old pupil mathematics exercise called the Cascades problem. Emotion is represented there as weighting values in the graph edges dynamically managed by an ant algorithm. The tests carried out on two versions, one emotional and the other one fully cognitive, show that the use of an emotional model produces a more efficient and adaptive solving. In addition, a second model named GAEA aims at simulating a robot equipped with sensors and effectors and thrown into a prey-predators environment inside which it must survive. Its behaviour is determined by its internal program that evolves thanks to a linear genetic program algorithm manipulating a population of program individuals. Results are promising and indicate that the population produces individuals whose behaviour is more and more adapted, and whose internal activity is analogous to the emergence of relevant emotional reactions.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Emotional processes modelling in decision making</title><title xml:lang="fr">Modélisation des processus émotionnel dans la prise de décision</title><author role="aut"><persName><forename type="first">Karim</forename><surname>Mahboub</surname></persName><email>karim.mahboub@gmail.com</email><idno type="halauthor">446572</idno><affiliation ref="#struct-23832"></affiliation></author><editor role="depositor"><persName><forename>Karim</forename><surname>Mahboub</surname></persName><email>karim.mahboub@gmail.com</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2012-05-13 14:24:19</date><date type="whenModified">2012-05-14 10:04:09</date><date type="whenReleased">2012-05-14 10:04:09</date><date type="whenProduced">2011-11-22</date><date type="whenEndEmbargoed">2012-05-13</date><ref type="file" target="https://tel.archives-ouvertes.fr/tel-00696675/document"><date notBefore="2012-05-13"></date></ref><ref type="file" n="1" target="https://tel.archives-ouvertes.fr/tel-00696675/file/manuscript.pdf"><date notBefore="2012-05-13"></date></ref></edition><respStmt><resp>contributor</resp><name key="172979"><persName><forename>Karim</forename><surname>Mahboub</surname></persName><email>karim.mahboub@gmail.com</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">tel-00696675</idno><idno type="halUri">https://tel.archives-ouvertes.fr/tel-00696675</idno><idno type="halBibtex">mahboub:tel-00696675</idno><idno type="halRefHtml">Intelligence artificielle [cs.AI]. Université du Havre, 2011. Français</idno><idno type="halRef">Intelligence artificielle [cs.AI]. Université du Havre, 2011. Français</idno></publicationStmt><seriesStmt><idno type="stamp" n="UNIV-LEHAVRE">Université du Havre</idno><idno type="stamp" n="UNIV-ROUEN">Université de Rouen</idno><idno type="stamp" n="INSA-ROUEN">Institut National des Sciences Appliquées de Rouen</idno><idno type="stamp" n="LITIS">Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes</idno></seriesStmt><notesStmt></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Emotional processes modelling in decision making</title><title xml:lang="fr">Modélisation des processus émotionnel dans la prise de décision</title><author role="aut"><persName><forename type="first">Karim</forename><surname>Mahboub</surname></persName><email>karim.mahboub@gmail.com</email><idno type="halAuthorId">446572</idno><affiliation ref="#struct-23832"></affiliation></author></analytic><monogr><imprint><date type="dateDefended">2011-11-22</date></imprint><authority type="institution">Université du Havre</authority><authority type="supervisor">Cyrille Bertelle</authority></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="fr">French</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">emotion modelling</term><term xml:lang="en">problem solving</term><term xml:lang="en">ant colony algorithms</term><term xml:lang="en">linear genetic programming</term><term xml:lang="fr">modèles de l'émotion</term><term xml:lang="fr">résolution de problèmes</term><term xml:lang="fr">algorithmes de colonies de fourmis</term><term xml:lang="fr">programmation génétique linéaire</term></keywords><classCode scheme="halDomain" n="info.info-ai">Computer Science [cs]/Artificial Intelligence [cs.AI]</classCode><classCode scheme="halDomain" n="scco.comp">Cognitive science/Computer science</classCode><classCode scheme="halDomain" n="scco.psyc">Cognitive science/Psychology</classCode><classCode scheme="halTypology" n="THESE">Theses</classCode></textClass><abstract xml:lang="en">Emotion is inseparable from cognitive processes and therefore plays a major role in decision making. As a result, it is becoming increasingly important in today's scientific research. The aim of this thesis is to show the advantages of an emotional approach, and to prove that in certain cases computer models equipped with artificial emotions prove to be more efficient than their purely cognitive equivalents. Based on this observation, two emotional models were realised from different study perspectives. They underline the impact of the addition of an emotional dimension in the elaboration of a fast, adaptive and efficient decision. The first developed model uses a graph for strategies representation in order to solve a ten-year-old pupil mathematics exercise called the Cascades problem. Emotion is represented there as weighting values in the graph edges dynamically managed by an ant algorithm. The tests carried out on two versions, one emotional and the other one fully cognitive, show that the use of an emotional model produces a more efficient and adaptive solving. In addition, a second model named GAEA aims at simulating a robot equipped with sensors and effectors and thrown into a prey-predators environment inside which it must survive. Its behaviour is determined by its internal program that evolves thanks to a linear genetic program algorithm manipulating a population of program individuals. Results are promising and indicate that the population produces individuals whose behaviour is more and more adapted, and whose internal activity is analogous to the emergence of relevant emotional reactions.</abstract><abstract xml:lang="fr">L'émotion est indissociable des processus cognitifs et joue par conséquent un rôle majeur dans la prise de décision. De ce fait, elle occupe une place de plus en plus importante dans la recherche scientifique actuelle. L'objectif de cette thèse est de révéler l'intérêt que peut présenter une approche émotionnelle, et de prouver que des modèles informatiques dotés d'émotions artificielles peuvent dans certains cas s'avérer plus performants que leurs équivalents purement cognitifs. Partant de ce constat, deux modèles de l'émotion ont été réalisés sous différentes perspectives d'étude. Ils soulignent l'impact de l'ajout d'une dimension émotionnelle dans l'élaboration d'une décision rapide, efficace et adaptée. Le premier modèle développé utilise un graphe de représentation de stratégies afin de résoudre un exercice de mathématiques proposé à des élèves de CM2, intitulé "problème des Cascades". L'émotion y est représentée en tant que valuation des arêtes au sein du graphe, la dynamique de ce dernier étant assurée par un algorithme fourmi. Les tests effectués sur deux versions, l'une émotionnelle et l'autre purement cognitive, montrent que l'utilisation d'un modèle émotionnel permet une résolution plus efficace et adaptative. Par ailleurs, un second modèle, nommé GAEA vise à simuler un robot équipé de capteurs et effecteurs, et plongé dans un environnement proie-prédateurs au sein duquel il doit survivre. Son comportement est déterminé par son programme interne, évoluant grâce à un algorithme de programmation génétique linéaire manipulant une population d'individus-programmes. Les résultats sont prometteurs et indiquent une évolution de la population vers des individus au comportement de plus en plus adapté, et dont l'activité interne est analogue à l'émergence de réactions émotionnelles pertinentes.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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