Les réflexions menées autour de l'idée d'intelligence collective (ou collaborative) postulent que celle-ci émerge d'un système en interaction dont les dispositifs techniques, sociaux et sémiotiques sont fortement imbriqués ( PIERRE LEVY , 1994). Pour relever ce défi, cette thèse propose de conjuguer deux concepts innovants. D'abord celui d'espace collaboratif ubiquitaire pour catalyser les interactions entre groupes d'agents et services. Ensuite celui d'infrastructure à ressources distribuées pour coordonner, de façon dynamique, les ressources et la sécurité en fonction de critères de qualité de service souhaités par les groupes d'agents. Un espace collaboratif ubiquitaire intègre des processus artificiels pour réaliser du traitement intensif (simulations, fouille de données, etc.) et des interfaces multimodales, incluant la visualisation partagée en mode synchrone ou asynchrone, pour les interactions entre humains. En termes d'infrastructure, cette thèse explique pourquoi le WEB n'apporte pas une réponse adaptée à la problématique de l'intelligence collective et pourquoi nous nous sommes tournés vers GRID (GRILLE DE RESSOURCES INFORMATIQUES DISTRIBUÉES). Notamment, une propriété singulière de GRID est le service à état qui permet de coordonner des ressources distribuées de façon dynamique et sécurisée. Pour mettre ceci en évidence, nous avons développé le modèle d'architecture AGORA qui propose d'utiliser GRID pour déployer des espaces collaboratifs ubiquitaires. De cette façon, les groupes d'agents humains et artificiels s'auto-organisent dans un espace immanent où les ressources sont délivrées dynamiquement par l'infrastructure. Validé par une méthode empirique, ce modèle a été l'objet d'une élaboration itérative fondée sur des retours d'expérimentation avec des communautés d'utilisateurs. Au vu de l'avancement de nos investigations, une architecture GRID couplée avec des concepts propres aux systèmes multi-agents présente les caractéristiques nécessaires pour déployer AGORA à grande échelle avec une utilisation rationnelle des ressources, tout en offrant des garanties de sécurité et de haute disponibilité.

In my doctoral dissertation, I tackled two of the main open problems in swarm robotics: design and verification. I did so by using model checking.<p>Designing and developing individual-level behaviors to obtain a desired swarm-level goal is, in general, very difficult, as it is difficult to predict and thus design the non-linear interactions of tens or hundreds individual robots that result in the desired collective behavior. In my dissertation, I presented my novel contribution to the top-down design of robot swarms: property-driven design. Property-driven design is based on prescriptive modeling and model checking. Using property-driven design it is possible to design robot swarms in a systematic way, realizing systems that are "correct by design". I demonstrated property-driven design on two case-studies: aggregation and foraging.<p>Developing techniques to analyze and verify a robot swarm is also a necessary step in order to employ swarm robotics in real-world applications. In my dissertation, I explored the use of model checking to analyze and verify the properties of robot swarms. Model checking allows us to formally describe a set of desired properties of a system, in a more powerful and precise way compared to other mathematical approaches, and verify whether a given model of a system satisfies them. I explored two different approaches: the first based on Bio-PEPA and the second based on KLAIM. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

In this dissertation, we propose and study methods for information transfer within a swarm of mobile robots that coordinately move, or flock, in a common direction. We define information transfer as the process whereby robots share directional information in order to coordinate their heading direction. We identify two paradigms of information transfer: explicit information transfer and implicit information transfer. <p><p>In explicit information transfer, directional information is transferred via communication. Explicit information transfer requires mobile robots equipped with a a communication device. We propose novel communication strategies for explicit information transfer, and we perform flocking experiments in different situations: with one or two desired directions of motion that can be static or change over time. We perform experiments in simulation and with real robots. Furthermore, we show that the same explicit information transfer strategies can also be applied to another collective behavior: collective transport with obstacle avoidance. <p><p>In implicit information transfer, directional information is transferred without communication. We show that a simple motion control method is sufficient to guarantee cohesive and aligned motion without resorting to communication or elaborate<p>sensing. We analyze the motion control method for its capability to achieve flocking with and without a desired direction of motion, both in simulation and using real robots. Furthermore, to better understand its underlying mechanism, we study this<p>method using tools of statistical physics, showing that the process can be explained in terms of non-linear elasticity and energy-cascading dynamics. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

In swarm robotics, the control of a group of robots is often fully distributed and does not rely on any leader. In this thesis, we are interested in understanding how to design collective decision making processes in such groups. Our approach consists in taking inspiration from nature, and especially from self organization in social insects, in order to produce effective collective behaviors in robot swarms. We have devised four robotics experiments that allow us to study multiple facets of collective decision making. The problems on which we focus include cooperative transport of objects, robot localization, resource selection, and resource discrimination. <p><p>We study how information is transferred inside the groups, how collective decisions arise, and through which particular interactions. Important properties of the groups such as scalability, robustness, and adaptivity are also investigated. We show that collective decisions in robot swarms can effectively arise thanks to simple mechanisms of imitation and amplification. We experimentally demonstrate their implementation with direct or indirect information transfer, and with robots that can distinguish the available options partially or not at all. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

We investigate the use of self-assembly in a robotic system as a means of responding<p>to different environmental contingencies. Self-assembly is the mechanism through which<p>agents in a multi-robot system autonomously form connections with one another to create<p>larger composite robotic entities. Initially, we consider a simple response mechanism<p>that uses stochastic self-assembly without any explicit control over the resulting morphology<p> / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

In this thesis, we investigate the problem of path formation and prey retrieval in a swarm of robots. We present two swarm intelligence control mechanisms used for distributed robot path formation. In the first, the robots form linear chains. We study three variants of robot chains, which vary in the degree of motion allowed<p>to the chain structure. The second mechanism is called vectorfield. In this case,<p>the robots form a pattern that globally indicates the direction towards a goal or<p>home location. Both algorithms were designed following the swarm robotics control<p>principles: simplicity of control, locality of sensing and communication, homogeneity<p>and distributedness.<p><p>We test each controller on a task that consists in forming a path between two<p>objects—the prey and the nest—and to retrieve the prey to the nest. The difficulty<p>of the task is given by four constraints. First, the prey requires concurrent, physical<p>handling by multiple robots to be moved. Second, each robot’s perceptual range<p>is small when compared to the distance between the nest and the prey; moreover,<p>perception is unreliable. Third, no robot has any explicit knowledge about the<p>environment beyond its perceptual range. Fourth, communication among robots is<p>unreliable and limited to a small set of simple signals that are locally broadcast.<p><p>In simulation experiments we test our controllers under a wide range of conditions,<p>changing the distance between nest and prey, varying the number of robots<p>used, and introducing different obstacle configurations in the environment. Furthermore,<p>we tested the controllers for robustness by adding noise to the different sensors,<p>and for fault tolerance by completely removing a sensor or actuator. We validate the<p>chain controller in experiments with up to twelve physical robots. We believe that<p>these experiments are among the most sophisticated examples of self-organisation<p>in robotics to date. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

The intention of the dissertation is the applied research of the collective ( group ) ( swarm ) intelligence . To demonstrate the applicability of the collective intelligence, the Particle Swarm Optimization ( PSO ) algorithm has been studied in which the problem of the collective intelligence is transferred to mathematical optimization in which the particle swarm searches for a global optimum within the defined problem space, and the searching is controlled according to the pre-defined objective function which represents the solved problem. A new search strategy has been designed and experimentally tested in which the particles continuously adjust their behaviour according to the characteristics of the problem space, and it has been experimentally discovered how the impact of the objective function representing a solved problem manifests itself in the behaviour of the particles. The results of the experiments with the proposed search strategy have been compared to the results of the experiments with the reference version of the PSO algorithm. Experiments have shown that the classical reference solution, where the only condition is a stable trajectory along which the particle moves in the problem space, and where the influence of a control objective function is ultimately eliminated, may fail, and that the dynamic stability of the trajectory of the particle itself is not an indicator of the searching ability nor the convergence of the algorithm to the true global solution of the solved problem. A search strategy solution has been proposed in which the PSO algorithm regulates its stability by continuous adjustment of the particles behaviour to the characteristics of the problem space. The proposed algorithm influenced the evolution of the searching of the problem space, so that the probability of the successful problem solution increased.

Physical processes rely on the transmission of energy and information across scales. In the last century, theoretical tools have been developed in the field of statistical physics to infer macroscopic properties starting from a microscopic description of the system. However, less attention has been devoted to the remodelling of microscopic degrees of freedom by macroscopic feedback. In recent years, ideas from non-equilibrium physics have been applied to characterise biological and artificial intelligence systems. These systems share in common their structure in discrete scales of organisation that perform specialised functions. To correctly regulate these functions, the accurate transmission of information across scales is crucial. In this thesis we study the role of macroscopic feedback in the remodelling of microscopic degrees of freedom in two paradigmatic examples, one taken from the field of biology, the self-organisation of specialisation and plasticity in a social wasp, and one from artificial intelligence, the remodelling of deep neural networks in a stochastic many-particle system. In the first part of this thesis we study how the primitively social wasp Polistes canadensis simultaneously achieves robust specialization and rapid plasticity. Combining a unique experimental strategy correlating time-resolved measurements across vastly different scales with a theoretical approach, we characterise the re-establishment of the social steady state after queen removal. We show that Polistes integrates antagonistic processes on multiple scales to distinguish between extrinsic and intrinsic perturbations and thereby achieve both robust specialisation and rapid plasticity. Furthermore, we show that the long-term stability of the social structure relies on the regulation of transcriptional noise by dynamic DNA methylation. In the second part of this thesis, we ask whether emergent collective interactions can be used to remodel deep neural networks. To this end, we study a paradigmatic stochastic manyparticle model where the dynamics are defined by the reaction rates of single particles, given by the output of distinct deep neural networks. The neural networks are in turn dynamically remodelled using deep reinforcement learning depending on the previous history of the system. In particular, we implement this model as a one dimensional stochastic lattice gas. Our results show the formation of two groups of particles that move in opposite directions, diffusively at early times and ballistically over longer time-scales, with the transition between these regimes corresponding to the time-scale of left/right symmetry breaking at the level of individual particles. Over a hierarchy of characteristic time-scales these particles develop emergent, increasingly complex interactions characterised by short-range repulsion and long-range attraction. As a result, the system asymptotically converges to a regime characterised by the presence of anti-ferromagnetic particle clusters. To conclude, we characterise the impact of memory effects and demographic disorder on the dynamics. Together, our results shed light on how non-equilibrium systems can employ macroscopic feedback to regulate the propagation of fluctuations across scales.

Ce travail présente une méthode de distribution dynamique et adaptative, pour des applications distribuées constituées de multiples entités en interaction, dans un environnement de calcul versatile. L'équilibrage de charge ainsi que la minimisation des coûts de communication sont pris en compte. La méthode proposée repose sur la détection d'organisations au sein de l'application afin de mieux la distribuer. Les organisations sont identifiées comme des groupes d'entités en très forte communication. Les organisations évoluent, apparaissent, se renforcent, s'affaiblissent et disparaissent. Les ressources disponibles de calcul sur lesquelles l'application s'exécutent varient également. Ces contraintes imposent à la distribution de s'adapter dynamiquement. La méthode est basée sur des colonies de fourmis numériques qui tentent de recruter les entités de l'application. Les fourmis coopèrent au sein d'une même colonie et sont en compétition lorsqu'elles n'appartiennent pas à une même colonie. Elles tentent de s'approprier les organisations au sein de l'application, chaque colonie travaillant pour une ressource de calcul distincte. La compétition inter-colonies permet la répartition de la charge. La collaboration au sein de chaque colonie permet la détection des organisations, en plaçant les très fortes communications ensembles sur la même ressource de calcul. Enfin la gestion de la population permet de prendre en compte l'hétérogénéité des ressources de calcul.

La mobilisation contre les gaz de schiste vécue actuellement au Québec s'inscrira certainement comme un moment fort dans la longue histoire des mouvements sociaux concernant l'exploitation des ressources naturelles. Ce mouvement de contestation face à cette industrie qui s'installait graduellement dans le paysage de différentes régions sans préavis, est né de la frustration de citoyen(ne)s qui se sont regroupés, appuyés par des groupes environnementaux déjà établis. Il a bénéficié du contexte particulier du Québec, et ce aux niveaux géographique, légal, politique et social, par rapport aux autres provinces ou pays où l'industrie a tenté ou est parvenu à s'installer. Il nous apparaît particulièrement intéressant puisqu'il nous permet d'observer comment des citoyen(ne)s n'ayant pas nécessairement d'antécédents militants se sont mobilisé(e)s à travers la province pour réclamer des études plus approfondies et des consultations publiques. Il nous permet ainsi, à travers la sociologie des mouvements sociaux et, plus particulièrement les théories entourant les nouveaux mouvements sociaux, de mettre à l'épreuve le concept d'écocitoyenneté, un concept de plus en plus utilisé, mais qu'on s'est peu attardé à définir. Ce dernier nous permettra d'étudier comment l'attachement à un territoire couplé d'un souci de préservation de l'environnement peut servir de tremplin au développement d'une citoyenneté intelligente et active. Plus largement, nous verrons comment ce mouvement, s'écartant de courants plus radicaux tels que l'écologie politique, est révélateur d'une nouvelle dynamique contestataire basée sur le développement d'une intelligence citoyenne et de compétences à l'action collective et sur l'instrumentalisation des mécanismes institutionnels. Nous nous attarderons également au répertoire d'actions collectives qui nous permet d'appréhender plus pragmatiquement l'efficience de cette mobilisation. Pour ce faire, nous avons rencontré trois militants dans le cadre d'entretiens semi-dirigés et avons analysé leur discours en utilisant la méthode de la théorisation ancrée. L'étude de ce cas, présenté comme un exemple de réussite, nous permettra d'entrevoir l'avenir des mouvements sociaux visant la protection de l'environnement au Québec. ______________________________________________________________________________ MOTS-CLÉS DE L’AUTEUR : nouveaux mouvements sociaux, écologie, environnement, gaz de schiste, écocitoyenneté, intelligence collective