Dans ce travail, nous proposons un système auto-organisé composé d'agents-robots contrôlés par une architecture de subsomption et des règles locales probabilistes de prises et de dépôts. Ces agents-robots sont capables, grâce au développement de leurs capacités cognitives de se créer une carte cognitive, d'apprendre plusieurs lieux buts et de planifier le retour vers ces buts. Bien que formellement l'algorithme ne permette pas à chaque agent de "mélanger ni de fusionner ou d'optimiser" plusieurs objectifs, nous montrerons que le système global est capable de réaliser une optimisation multi-objectifs. Particulièrement, la fusion de l'apprentissage local avec l'accumulation de décisions individuelles permet de faire émerger (i) des structures dans l'environnement et (ii) des dynamiques tel que le comportement de spécialisation ou les comportements que nous pouvons considérer comme étant "égoïstes" ou "altruistes". Nous montrons qu'un mécanisme d'imitation simple contribue à l'amélioration des performance de notre SMAC et de notre SMRC, à savoir l'optimisation de la durée pour découvrir des différentes ressources, le temps moyen de planification, le niveau global de satisfaction des agents et enfin le temps moyen de convergence vers une solution stable. Particulièrement, l'ajout d'une capacité d'imitation améliore la construction des cartes cognitives pour chaque agent et stimule le partage implicite des informations dans un environnement a priori inconnu. En effet, les découvertes individuelles peuvent avoir un effet au plan social et donc inclure l'apprentissage de nouveaux comportements au niveau individuel. Pour finir, nous comparons les propriétés émergentes de notre SMAC à un modèle mathématique basé sur la programmation linéaire (PL). Cette évaluation montre les bonnes performances de notre SMAC qui permet d'avoir des solutions proches des solutions de la PL pour un coût de calcul réduit. Dans une dernière série d'expériences, nous étudions notre système d'agrégation dans un environnement réel. Nous mettons en place un SMRC, composé par des robots qui sont capables d'effectuer les opérations de prise, de dépôt et de maintien. Nous montrons via les premiers tests d'agrégation que les résultats sont prometteurs. / In this work, we propose a self-organized system composed by agents-robots, controlled by a subsumption architecture with probabilistic local rules of deposits and taking. These agents-robots are able, thanks to the development of their cognitive abilities to create a cognitive map, to learn various goals' locations and to plan the return to these goals. Although formally the algorithm does not allow each agent to « mix or merge or optimize » several objectives, we show that the overall system is able to perform a multi-objective optimization. Specifically, the fusion of local learning with the accumulation of the individual decisions allows to emerge (i) structures in the environment and (ii) several dynamics such as specialization behavior or those that we can consider as « selfish » or « altruistic ». We show that the imitation strategy contributes to the improvement of the performance of our SMAC and our SMRC, namely the optimization of time to explore the various resources, the average planning time, the overall satisfaction level of agents and finally the the average time of convergence to a stable solution. Specifically, the addition of an imitation ability improves the construction of cognitive maps for each agent and stimulates the implicit sharing of informations in an unknown environment. Indeed, individual discoveries can affect the social level and therefore include learning new behaviors at the individual level. Finally, we compare the emergent properties of our SMAC with a mathematical model based on linear programming (LP). This evaluation shows the good performance of our SMAC which allows to obtain solutions close to the solution of the PL for a low cost of computation. In a final series of experiments, we study our aggregation system in a real environment. We set up a SMRC, composed by robots that are able to perform taking operations, deposits operations and refueling operations. We show through the first tests of aggregation that the results are promising.

Nanostructured materials are the next generation of high-performance materials, harnessing the novel properties of their nanosized constituents. The controlled assembly of nanosized particles and the design of the optimal nanostructure require a detailed understanding of particle interactions and robust methods to tune them. This thesis describes innovative approaches to these challenges, relating to the determination of Hamaker constants for iron oxide nanoparticles, the packaging of nanopowders into redispersible granules, the tuning of the wetting behavior of nanocrystals and the simulation of collective magnetic properties in arrays of superparamagnetic nanoparticles. The non-retarded Hamaker constants for iron oxides have been calculated from their optical properties based on Lifshitz theory. The results show that the magnitude of vdW interactions in non-polar solvents has previously been overestimated up to 10 times. Our calculations support the experimental observations that oleate-capped nanoparticles smaller than 15 nm in diameter can indeed form colloidally-stable dispersions in hydrocarbons. In addition, a simple procedure has been devised to remove the oleate-capping on the iron oxide nanoparticles, enabling their use in fluorometric assays for water remediation, with a sensitivity more than 100 times below the critical micelle concentration for non-ionic surfactants. Nanosized particles are inherently more difficult to handle in the dry state than larger micron-sized powders, e.g. because of poor flowability, agglomeration and potential toxicity. The rheology of concentrated slurries of TiO2 powder was optimized by the addition of sodium polyacrylate, and spray-dried into fully redispersible micron-sized granules. The polymer was embedded into the granules, where it could serve as a re-dispersing aid. Monte Carlo (MC) simulations have been applied to the collective magnetic behavior of nanoparticle arrays of various thicknesses. The decrease in magnetic susceptibility with the thickness observed experimentally was reproduced by the simulations. Ferromagnetic couplings in the arrays are enhanced by the finite thickness, and decrease in strength with increasing thickness. The simulations indicate the formation of vortex states with increasing thickness, along with a change in their orientation, which becomes more and more isotropic as the thickness increases. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 2: Manuscript.</p>