Éditeur(s) : HAL CCSD ACM
Résumé : International audience
In this study, we use an adapted version of Pitsburgh-like learning classifier system to perform over classification tasks. The Adapted Pittsburgh Classifier System, enhanced with a new mechanism, allows us to consider the classification problems and their treatment by a given LCS in a different manner. Our aim is to exhibit elements in order to prove that, using the action covering mechanism, this system is able to build an inner map of a given classification learning sample. In the context of this paper, this map is built using an intrisic property of Pittsburgh-like CS: the use of various collections of classifiers amongst a unique population.
Proceedings of the 12th annual conference comp on Genetic and evolutionary computation
Portland,Oregon, United States

hal-00520609
https://hal.archives-ouvertes.fr/hal-00520609
DOI : 10.1145/1830761.1830823

Éditeur(s) : HAL CCSD ACM New York, NY, USA
Résumé : International audience
This paper focuses on the study of the behavior of a genetic algorithm based classifier system, the Adapted Pittsburgh Classifier System (A.P.C.S), on maze type environments containing aliasing squares. This type of environment is often used in reinforcement learning litterature to assess the performances of learning methods when facing problems containing non markov situations. Through this study, we discuss on the performances of the APCS on maze type environments and also of the efficiency of an improvement of the APCS learning method inspired from the XCS : the covering mechanism. We manage to show that, without any memory mechanism, the APCS is able to build and to keep accurate strategies to produce regular sub-optimal solutions to these maze problem. This statement is shown through a comparison of the results obtained by the XCS, XCSM and XCSMH on distinct maze problems with these obtained by the APCS.
Proceedings of the 2008 GECCO conference companion on Genetic and evolutionary computation
Atlanta, GA, United States

hal-00520607
https://hal.archives-ouvertes.fr/hal-00520607
DOI : 10.1145/1388969.1389013

Éditeur(s) : HAL CCSD ACM
Résumé : International audience
In 2008 a scientific paper written by Butz and al. investigated Matching in Learning Classifier Systems. Matching represents at least 65% of the computational time when executing a classifier system as reported in Llora and al. We propose to modify that encoding using a fast and accurate matching replacing standard matching algorithm for character-based classifier systems.
Proceedings of the 12th annual conference comp on Genetic and evolutionary computation
Portland, Oregon, United States

hal-00520610
https://hal.archives-ouvertes.fr/hal-00520610
DOI : 10.1145/1830761.1830820

Éditeur(s) : HAL CCSD
Résumé : International audience
This paper focuses on the study of the influence of a newly implemented mechanism on a Pittsburgh-like classifier system. The Adapted Pittsburgh Classifier System is a learning classifier system that uses genetic algorithms to evolve its ruleset. The new mechanism discussed is inspired from Wilson work on the eXtended Classifier System (XCS): it allows the concerned LCS to adapt its rule set when facing a new signal by modifying an existing rule. This mechanism is called covering mechanism due to the fact that the rule is covered up by a new rule which is sensitive to this new signal. Effects of this covering mechanism are first measured on the performances of APCS on well known multi-step environments: maze-type environments (Woods 101 and E2). In addition, further measures presented in this paper indicate that we could possibly rely on the behavior of this covering mechanism to automate the research of a correct cognitive capacity needed by the APCS to solve a given multistep problem.
2008 Eighth International Conference on Hybrid Intelligent Systems
Barcelone, Spain

hal-00520608
https://hal.archives-ouvertes.fr/hal-00520608
DOI : 10.1109/HIS.2008.105

Éditeur(s) : HAL CCSD
Résumé : J'étais à l'époque dans le laboratoire I3S de Sophia-Antipolis. Je suis dorénavant au laboratoire GRIMAAG en Guadeloupe.
We focused on the communication problem within multi-agents systems, inspirited by nature and more precisely by the ants that are a perfect example of living integrating learning (ontogenesis) to genetic inheritance (phylogenesis): the Baldwin Effect.
We worked with agents represented by classifiers systems using genetic algorithm. We introduced a new fixed knowledge based classifiers system, the simplified Pittsburgh classifier system, which we studied in a spontaneous coordination multi-agents problem. Then, we successfully allowed “homogeneous” agents to communicate their “knowledge” using distributed elitism.
Finally, “heterogeneous” agents efficiently exchanged information thanks to a minimal communication model we defined, bound to reality and extended.
Nous nous sommes intéressés à la problématique de la communication au sein des systèmes multi-agents en nous inspirant de la nature et plus particulièrement des fourmis qui sont un parfait exemple de l'intégration de l'apprentissage du vivant (ontogenèse) au capital génétique de celui-ci (phylogénie) : l'effet Baldwin.
Nous avons choisi de travailler avec des systèmes de classeurs à algorithme génétique pour représenter les agents. Nous avons introduit un nouveau type de système de classeurs à base de connaissance de taille fixe, les systèmes de classeurs de type Pittsburgh simplifié, dont nous avons étudié les propriétés dans un contexte multi-agents de coordination spontanée.
Nous avons ensuite proposé avec succès aux agents "homogènes" de communiquer pour échanger leur "connaissance" à l'aide de l'élitisme distribué.
Enfin, les agents "hétérogènes" ont échangé efficacement des informations grâce à un modèle minimal de communication que nous avons ancré dans la réalité puis étendu.
https://tel.archives-ouvertes.fr/tel-00192372

tel-00192372
https://tel.archives-ouvertes.fr/tel-00192372
https://tel.archives-ouvertes.fr/tel-00192372/document
https://tel.archives-ouvertes.fr/tel-00192372/file/These.pdf

Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
The number of satellites and sensors devoted to Earth observation has become increasingly elevated, delivering extensive data, especially images. At the same time, the access to such data and the tools needed to process them has considerably improved. In the presence of such data flow, we need automatic image interpretation methods, especially when it comes to the monitoring and prediction of environmental and societal changes in highly dynamic socio-environmental contexts. This could be accomplished via artificial intelligence. The concept described here relies on the induction of classification rules that explicitly take into account structural knowledge, using Aleph, an Inductive Logic Programming (ILP) system, combined with a multi-class classification procedure. This methodology was used to monitor changes in land cover/use of the French Guiana coastline. One hundred and fifty-eight classification rules were induced from 3 diachronic land cover/use maps including 38 classes. These rules were expressed in first order logic language, which makes them easily understandable by non-experts. A 10-fold cross-validation gave significant average values of 84.62%, 99.57% and 77.22% for classification accuracy, specificity and sensitivity, respectively. Our methodology could be beneficial to automatically classify new objects and to facilitate object-based classification procedures.
ISSN: 0098-3004

hal-01369822
https://hal.archives-ouvertes.fr/hal-01369822
https://hal.archives-ouvertes.fr/hal-01369822/document
https://hal.archives-ouvertes.fr/hal-01369822/file/CAGEO-D-13-00538R1_article.pdf
DOI : 10.1016/j.cageo.2014.08.013

Éditeur(s) : HAL CCSD IRD
Résumé : International audience
The number of satellites and remote sensing sensors devoted to earth observation becomes increasingly high, providing more and more data and especially images. In the same time the access to such a data and to the tools to process them has been considerably improved. In the presence of such data flow - and regarding the necessity to follow up and predict environmental and societal changes in highly dynamic socio-environmental contexts - we need automatic image interpretation methods. This could be accomplished by exploring some strengths of artificial intelligence. Our main idea consists in inducing classification rules that explicitly take into account structural knowledge, using Aleph, an Inductive Logic Programming (ILP) system. We applied our proposed methodology to three land cover/use maps of the French Guiana littoral. One hundred and forty six classification rules were induced for the 39 land-cover classes of the maps. These rules are expressed in first order logic language which make them intelligible and interpretable by non-experts. A ten-fold cross validation gave average values for classification accuracy, specificity and sensibility equal to, respectively, 98.82 %, 99.65% and 70%. The proposed methodology could be valuably exploited to automatically classify new objects and/or help operators using object-based classification procedures.
Symposium of the Latin American Society for Remote Sensing and Spatial Information Systems (SELPER)
Cayenne, French Guiana

ird-00797337
http://hal.ird.fr/ird-00797337
http://hal.ird.fr/ird-00797337/document
http://hal.ird.fr/ird-00797337/file/Bayoudh_et_al_2012_FP_SELPER-086.pdf
IRD : fdi:010058343

Éditeur(s) : HAL CCSD
Résumé : International audience
“Affective Computing is computing that relates to, arises from, or deliberately influences emotions” (R.W. Picard, MIT Press, 1997). Specifically, psychosocial equilibrium is now considered as a major issue for both individual and public health. Predicting human emotions via non-intrusive methods is a great challenge triggered by the rise of intelligent assisting systems. In order to determine the emotional state of a subject having a potential psychosocial disorder, it is required to process both neutral and expressive emotions. A computer-engineered method aiming to provide emotional information from videos is proposed. An original approach for affect classification and its interpretation is presented.The database is part of the HUMAINE database, and consists of a large volume of video clips, each displaying individuals, depressed or not, talking with a conversational agent. Both the high-dimensionality of data and its temporal aspect require a pre-training that consists of cleaning, sorting and feature selection, in order to make relevant information usable for further processing by MachineLearning methods. Next step consists in classifying the affective state of subjects according to their positivity or negativity (Russell's model, 1980). By means of six independent classifiers, each running a Support Vector Machine algorithm, separate performances are obtained for each class of emotions. The results reveal an interesting “neutral area phenomenon”, where emotions tend to be harder to be detected.Though more frequent, neutral emotions seem to carry less discriminant information than expressive ones. This issue will be a key for designing a tuning step in further research.
Caribbean Academy Of Science
Deshaies, France

hal-01408029
https://hal.univ-antilles.fr/hal-01408029

Éditeur(s) : HAL CCSD
Résumé : International audience
Le nombre de satellites et de capteurs pour la télédétection dédiés à l'observation de la Terre ne cesse d'augmenter, permettant ainsi d'avoir une masse de données importante en particulier en matière d'images. Parallèlement, un effort permanent vise, d'une part, à améliorer l'accès à ces données et, d'autre part, à développer d'avantages d'outils pour les manipuler. De tels efforts sont particulièrement utiles dans des contextes socio-environnementaux très dynamiques spatialement et temporellement, pour lesquels il est nécessaire de suivre et de prédire les événements environnementaux et sociétaux. En revanche, en présence d'un tel flux de données, nous avons besoin de méthodes automatiques d'interprétation d'images. Une solution envisageable pour répondre à ce besoin est de bénéficier des atouts de l'intelligence artificielle pour l'obtention de cartes d'occupation du sol issues d'une classification des régions des images. Afin de contribuer à l'automatisation de la classification, nous proposons une méthode d'induction de règles interprétables par des non-experts et mettant en évidence, explicitement, des connaissances structurelles. Cette méthode s'appuie sur la programmation logique inductive (PLI) et en particulier sur le système inductif ”Aleph”. L'application de la méthode de classification Multi-class Rule Set Intersection (MRSI) permet ensuite de classifier tout nouvel objet au regard des ses caractéristiques intrinsèques et de celles des objets environnants. Nous avons appliqué notre méthodologie à l'étude de la dynamique du littoral de la Guyane Française. Suite à ce travail, nous avons induit 136 règles de classification pour 38 classes d'occupation du sol. Ces règles sont intelligibles et simples à interpréter de par l'utilisation de la logique du premier ordre. Les performances du système ont été évaluées par la validation croisée. En moyenne, la précision, la spécificité et la sensibilité sont, respectivement, égales à 84,62%, 99,57% et 77,22%. Ces résultats quantitatifs montrent une bonne performance de la méthodologie pour la mise à jour automatique de cartes d'occupation du sol et/ou l'assistance aux opérateurs utilisant l'analyse d'image orientée-objet.
11èmes Rencontres des Jeunes Chercheurs en Intelligence Artificielle
Lille, France

hal-01376400
https://hal.archives-ouvertes.fr/hal-01376400
https://hal.archives-ouvertes.fr/hal-01376400/document
https://hal.archives-ouvertes.fr/hal-01376400/file/Bayoudh_RJCIA2013.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
This paper focuses on the study of the behaviour of a singular classifier system, the Adapted Pittsburgh Classifier System (A.P.C.S), on environments containing aliasing situations. Maze type environments are often used in reinforcement learning literature to assess the performances of learning methods when facing problems containing non Markovian situations. Those situations are often encountered when performing reinforcement learning on aliased data samples issued from meteorological simulations.
International Journal of Artificial Intelligence

hal-00520611
https://hal.archives-ouvertes.fr/hal-00520611

Résumé : Ce travail vise à mettre en place un système de fusion de sources d'information basé sur la Théorie des Fonctions de Croyance (TFC) et adapté aux contextes des images forestières. La TFC engendre des temps de calcul importants lorsque la cardinalité du cadre de discernement devient grande. Ainsi, nous avons proposé en premier lieu une méthode de réduction du référentiel de définition permettant d'éviter l'explosion combinatoire en restreignant le calcul sur les hypothèses estimées pertinentes et déterminées à partir des histogrammes des classes issues de la phase d'apprentissage. Ensuite, nous avons proposé des techniques d'estimation de fonctions de masse spectrales, texturales, contextuelles et structurelles. L'information multi-fractale a été utilisée comme signature de texture, ainsi l'image de Hôlder a constitué une source d'information pour l'estimation de la masse texturale. Un autre apport consiste à proposer une approche de transfert de masse basée sur l'information contextuelle, en exploitant l'image des couronnes d'arbres comme une source déduite. L'implémentation d'algorithmes de détection de couronnes d'arbres nous a permis d'extraire des mesures sur les couronnes que nous avons utilisées en tant qu'attributs pour l'estimation des fonctions de masse. Nos expérimentations montrent des taux de classification assez satisfaisants et une adéquation de notre approche de fusion à une classification à l'arbre près
This thesis aims to develop an information source fusion system based on belief functions theory (BFT) and adapted t forest images context (especially high resolution images). Applications of BFT to image classification result in th assignment of each image point to a c1ass or a subset of classes based on the mass functions estimation expressing th belief of an event. BFT leads to significant computation time when the cardinality of the set of definition become large. Thus, we first proposed a method to reduce the frame of discernment in order te avoid the combinatorial explosio by restricting the calculation on the hypotheses of the set of definition deduced from the classes histograms determine from the learning phase. Then, we proposed spectral, texturai, contextual and structural mass functions technique estimation. Multi-fractal information was used as texture signature and Hôlder image has been a source ( information for estimating the texturai mass. Another contribution is the integration of trees crowns image as derive source and the proposai of an approach to mass transfer based on contextual information inside and outside a crown. Th implementation of detection trees crowns algorithms allowed us to extract measurements on trees. These measurement are used as attributes for mass functions estimation. Our experiments and comparisons with other methods show quit satisfactory classification rate with spectral and textural approaches and adequacy of our fusion approach to contextu and structural information for trees classification

http://www.theses.fr/2011AGUY0434/document