Etablir le meilleur plan pour l'usinage d'un produit, le meilleur ordonnancement des activités de construction d'un bâtiment ou la meilleure tournée de véhicules pour la livraison des commandes, en prenant en compte diverses contraintes économiques, temporelles, humaines, ou même météorologiques : dans cette diversité d'applications, optimiser la planification est une tâche complexe par le grand nombre d'entités hétérogènes en interaction, la forte dynamique, les objectifs contradictoires à atteindre, etc. La planification de missions pour des constellations de satellites en est un exemple majeur : beaucoup de paramètres et de contraintes, souvent antagonistes, doivent être pris en compte, entraînant une importante combinatoire. Actuellement, en Europe, les plans de missions sont élaborés au sol, juste avant que le satellite ne soit visible par la station d'émission. Les requêtes arrivant durant la planification ne peuvent être traitées, et sont mises en attente. De plus, la complexité de ce problème croit drastiquement : le nombre de constellations et les satellites les composant augmentent, ainsi que le nombre de requêtes journalières. Les approches actuelles montrent leurs limites. Pour pallier à ces inconvénients, de nouveaux systèmes basés sur la décentralisation et la distribution inhérentes à ce genre de problèmes, sont nécessaires. La théorie des systèmes multi-agents adaptatifs (AMAS) et notamment le modèle AMAS4Opt (AMAS for Optimisation) ont montré leur adéquation pour la résolution de problèmes d'optimisation complexes sous contraintes. Le comportement local et coopératif des agents AMAS permet au système de s'auto-adapter à la forte dynamique et de fournir des solutions adéquates rapidement. Dans cette thèse, nous adressons la résolution de la planification des missions de satellites par AMAS. Pour cela, nous avons complété et enrichi les modèles d'agents proposés par AMAS4Opt. Nous avons ainsi développé le système de planification dynamique de missions ATLAS. Pour valider ATLAS sur divers critères, nous avons utilisé un grand nombre de données hétérogènes. Enfin, ce travail a été comparé à un système " opérationnel' " standard sur des scénarios réels, mettant en valeur les apports de notre système. / Building the best plan in product treatment, the best schedule to a building construction or the best route for a salesman in order to visit a maximum of cities in the time allowed while taking into account different constraints (economic, temporal, humans or meteorological ): in all of those variety of applications, optimizing the planning is a complex task including a huge number of heterogeneous entities in interaction, the strong dynamics, multiple contradictory objectives, etc. Mission planning for constellations of satellites is a major example: a lot of parameters and constraints, often antagonists must be integrated, leading to an important combinatorial search space. Currently, in Europe, plans are built on ground, just before the satellite is visible by the ground stations. Any request coming during the planning process must wait for the next period. Moreover, the complexity of this problem grows drastically: the number of constellations and satellites increases, as the number of daily requests. Current approaches have shown their limits. To overcome those drawbacks, new systems based on decentralization and distribution inherent to this problem, are needed. The adaptive multi-agent systems (AMAS) theory and especially the AMAS4Opt (AMAS For Optimization) model have shown their adequacy in complex optimization problems solving. The local and cooperative behavior of agents allows the system to self-adapt to highly dynamic environments and to quickly deliver adequate solutions. In this thesis, we focus on solving mission planning for satellite constellations using AMAS. Thus, we propose several enhancement for the agent models proposed by AMAS4Opt. Then, we design the ATLAS dynamic mission planning system. To validate ATLAS on several criteria, we rely on huge sets of heterogeneous data. Finally, this work is compared to an operational and standard system on real scenarios, highlighting the value of our system.

This thesis applies predictor-based methods for the distributed consensus control of multi-agent systems with input delay. "Multi-agent systems" is a term used to describe a group of agents which are connected together to achieve specified control tasks over a communication network. In many applications, the subsystems or agents are required to reach an agreement upon certain quantities of interest, which is referred to as "consensus control". This input delay may represent delays in the network communication. The main contribution of this thesis is to provide feasible methods to deal with the consensus control for general multi-agent systems with input delay. The consensus control for general linear multi-agent systems with parameter uncertainties and input delay is first investigated under directed network connection. Artstein reduction method is applied to deal with the input delay. By transforming the Laplacian matrix into the real Jordan form, delay-dependent conditions are derived to guarantee the robust consensus control for uncertain multi-agent systems with input delay. Then, the results are extended to a class of Lipschitz nonlinear multi-agent systems and the impacts of Lipschitz nonlinearity and input delay in consensus control are investigated. By using tools from control theory and graph theory, sufficient conditions based on the Lipschitz constant are identified for proposed protocols to tackle the nonlinear terms in the system dynamics. Other than the time delay, external disturbances are inevitable in various practical systems including the multi-agent systems. The consensus disturbance rejection problems are investigated. For linear multi-agent systems with bounded external disturbances, Truncated Predictor Feedback (TPF) approach is applied to deal with the input delay and the H_infinity consensus analysis is put in the framework of Lyapunov analysis. Sufficient conditions are derived to guarantee the H_infinity consensus in time domain. Some disturbances in real engineering problems have inherent characteristics such as harmonics and unknown constant load. For those kinds of disturbances in Lipschitz nonlinear multi-agent systems with input delay, Disturbance Observer-Based Control (DOBC) technique is applied to design the disturbance observers. A new predictor-based control scheme is constructed for each agent by utilizing the estimate of the disturbance and the prediction of the relative state information. Sufficient delay-dependent conditions are derived to guarantee consensus with disturbance rejection.

La Tarification à l’Activité (T2A) contraint les services d’urgences hospitalières à développer différentes stratégies d’'allocation efficiente des ressources. L'optimisation de la prise en charge est centrale à cette problématique et vise des coûts de production couverts par les revenus induits par la T2A. Aussi, l'objectif de la thèse est d’identifier l’apport d’un Système d’Aide à la Décision (SAD) basé sur les Systèmes Multi-Agents (SMA) utilisant une modélisation basée sur un algorithme d'ordonnancement des moyens de production des soins en trois phases (OR-3P). Cette modélisation formalise l'organisation de ces services autour de cinq types d'agents. L’Agent Ordonnanceur y chargé d’affecter les personnels de l’équipe médicale et de gérer les flux de patients. Il joue un rôle prépondérant dans la recherche d’optimisation. Les résultats obtenus de l’application simulée de l’OR-3P, montrent l’optimisation des délais d’attente et de passage global, une augmentation de la productivité et une indication qualitative du bon fonctionnement de la prise en charge. Ces résultats incitent à réaliser des expérimentations dans des établissements français. / The activity-based payment, which is known in France as T2A requires hospital emergency departments, faced with a very strong growth in their activities since 1990, to develop strategies of an efficient allocation of resources. The optimization of medical treatment is central to this issue and should allow obtaining production costs covered by funding induced T2A. The main objective of the thesis was to identify the contribution of a System Decision Support (DSS) based on Multi-Agent Systems (MAS), using multi-agent modelling of care means production, using a three-phase scheduling algorithm, so called OR-3P. This modelling formalizes the organization of emergency departments around five types of agent. The scheduler Officer is responsible for assigning personnel to the medical team in an efficient framework for managing patient flow, plays the leading role in the search of optimized management. Results from the application of OR-3P, show an optimizing of the delays and the overall passage, an increase in productivity, a qualitative indication of proper functioning. These results lead to tests in French institutions.

Le contexte informationnel dans lequel évolue un agent possède une importance extrême quandcelui-ci élabore son comportement futur. Un agent rationnel doit en effet baser ses choix sur les informationsqu’il possède pour choisir ses actions. Or, dans les applications réelles, l’information disponible àl’agent est souvent rare et peu précise. De multiples modèles ont été élaborés dans les différents cadresd’application de l’intelligence artificielle afin de caractériser une décision rationnelle dans chacun descontextes informationnels possibles. Les travaux présentés dans cette thèse concernent l’élaboration d’unmodèle permettant à un agent de prendre des décisions rationnelles dans un contexte informationnel trèspauvre. La seule information dont dispose un agent à propos du résultat de ses actions est la donnée del’ensemble de résultats de chacune d’entre elles. En particulier, aucune information sur la conséquence laplus susceptible de se produire n’est disponible. L’agent est supposé égoïste (au sens où seul compte pourlui son propre intérêt) et autonome. Il évolue de plus dans un environnement où il coexiste avec d’autresagents (qui sont aussi égoïstes et autonomes). Les actions d’un agent influent sur les autres agents. Ladémarche entreprise pour élaborer le modèle est la suivante. D’abord, nous caractérisons les critères dedécision rationnels d’un agent seul dans le contexte informatif étudié. Ensuite, nous étendons ces critèresde décision individuelle au cas multi-agent en nous appuyant sur la théorie des jeux qui est le meilleurcadre pour exprimer les interactions entre agents rationnels et en particulier les possibilités de coordinationentre les agents. Enfin, le domaine de la planification est un excellent cadre pour représenter etexprimer les concepts du modèle. / The informative context in which an agent evolves is extremely important when she elaborates her futurebehaviour. A rational agent must base her choices on the available information. In realistic applications,the information is often rare and imprecise. Many models have been introduced to caracterize rationaldecision in each possible informative context. This thesis is about the elaboration of a model that allowsan agent to make rational decisions in an extremely poor informative context. The only informationthat is available to an agent about her actions’ consequences is the result set of each of her actions. Noinformation about which consequence of any action will eventually happen is available. The agent issupposed to be selfish (which means that her own interest is her only concern) and autonomous. Sheevolves in an environment in which she coexists with other agents (that are as selfish and autonomous asher). An agent action may inflence those of other agents. We used the following approach to build ourmodel. First, we caracterized the rational decision criteria for an agent to use in the context of completeignorance. Then we extended these criteria, by using game theory concepts, to a multiagent environment.Finally, the planning framework is an excellent framework to represent the introduced concepts.

Finding safe trajectories for multiagent autonomous systems can be difficult, especially as multiple robots and obstacles are added to the system. Control barrier functions (CBFs) have been effective in addressing this problem. Although the use of CBFs for guaranteeing safe operation is well established, there is no standard software implementation to simplify the integration of these techniques into robotic systems. We present a CBF Toolbox to fill this void. Although the CBF Toolbox can be used to ensure safety based on local control decisions, it may not be sufficient to guide a robots to their goals in certain environments. In these cases, path planning algorithms are required. We present one such algorithm, which is the multiagent extension of the CBF guided rapidly-exploring random trees (CBF-RRT) to demonstrate how the CBF Toolbox can be applied. This work addresses the theory behind the CBF Toolbox, as well as presenting examples of how it is applied to multiagent systems. Examples are shown for its use in both simulation and hardware experiments. Details are provided on CBF guided rapidly-exploring random trees (CBF-RRT), and its application to multiagent systems with multiagent CBF-RRT (MA-CBF-RRT) that streamlines safe path planning for teams of robots.

The flock-guidance problem enjoys a challenging structure where multiple optimization objectives are solved simultaneously. This usually necessitates different control approaches to tackle various objectives, such as guidance, collision avoidance, and cohesion. The guidance schemes, in particular, have long suffered from complex tracking-error dynamics. Furthermore, techniques that are based on linear feedback or output feedback strategies obtained at equilibrium conditions either may not hold or degrade when applied to uncertain dynamic environments. Relying on potential functions, embedded within pre-tuned fuzzy inference architectures, lacks robustness under dynamic disturbances. This thesis introduces two adaptive distributed approaches for the autonomous control of multi-agent systems. The first proposed technique has its structure based on an online fuzzy reinforcement learning Value Iteration scheme which is precise and flexible. This distributed adaptive control system simultaneously targets a number of flocking objectives; namely: 1) tracking the leader, 2) keeping a safe distance from the neighboring agents, and 3) reaching a velocity consensus among the agents. In addition to its resilience in the face of dynamic disturbances, the algorithm does not require more than the agent’s position as a feedback signal. The effectiveness of the proposed method is validated with two simulation scenarios and benchmarked against a similar technique from the literature. The second technique is in the form of an online fuzzy recursive least squares-based Policy Iteration control scheme, which employs a recursive least squares algorithm to estimate the weights in the leader tracking subsystem, as a substitute for the original reinforcement learning actor-critic scheme adopted in the first technique. The recursive least squares algorithm demonstrates a faster approximation weight convergence. The time-invariant communication graph utilized in the fuzzy reinforcement learning method is also improved with time-varying graphs, which can smoothly guide the agents to reach a speed consensus. The fuzzy recursive least squares-based technique is simulated with a few scenarios and benchmarked against the fuzzy reinforcement learning method. The scenarios are simulated in CoppeliaSim for a better visualization and more realistic results.

No / This intention of this chapter is to critically examine uses of the World Wide Web by fans of cult movies. It begins by outlining how cult movies are categorised, and notes the problems that this engenders. Then the relationship between technologies and subcultural practices is observed. Examples are presented to illustrate the question of whether, through remediation processes, such practices tell us anything new about forms of contemporary communication and consumption.

Yes / Modern manufacturing companies face increased pressures to adapt to shorter product life cycles and the need to reconfigure more frequently their production systems to offer new product variants. This paper proposes a new multi-agent architecture utilising “plug and produce” principles for configuration and reconfiguration of production systems with minimum human intervention. A new decision-making approach for system reconfiguration based on tasks re-allocation is presented using goal driven methods. The application of the proposed architecture is described with a number of architectural views and its deployment is illustrated using a validation scenario implemented on an industrial assembly platform. The proposed methodology provides an innovative application of a multi-agent control environment and architecture with the objective of significantly reducing the time for deployment and ramp-up of small footprint assembly systems. / The reported research has been part of the EU FP7 research project “PRIME”

Musical Agents are an emerging technology, designed to provide a range of new musical opportunities to human musicians and composers. Current systems in this area lack certain features which are necessary for a high quality musician; in particular, they lack the ability to structure their output in terms of a communicative dialogue, and reason about the responses of their partners. In order to address these issues, this thesis develops Musical Act Theory (MAT). This is a novel theory, which models musical interactions between agents, allowing a dialogue oriented analysis of music, and an exploration of intention and communication in the context of musical performance. The work here can be separated into four main contributions: a specification for a Musical Middleware system, which can be implemented computationally, and allows distributed agents to collaborate on music in real-time; a computational model of musical interaction, which allows musical agents to analyse the playing of others as part of a communicative process, and formalises the workings of the Musical Middleware system; MAMA, a musical agent system which embodies this theory, and which can function in a variety of Musical Middleware applications; a pilot experiment which explores the use of MAMA and the utility of MAT under controlled conditions. It is found that the Musical Middleware architecture is computationally implementable, and allows for a system which can respond to both direct musical communi- cation and extramusical inputs, including the use of a custom-built tangible interface. MAT is found to capture certain aspects of music which are of interest — an intuitive notion of performative actions in music, and an existing model of musical interaction. Finally, the fact that a number of different levels — theory, architecture and implementation — are tied together gives a coherent model which can be applied to many computational musical situations.

As Chinese economy develops, service innovation has become a key element for which Chinese enterprises compete and upgrading of service industry all over China due to its influence on national economic competitiveness, among which the innovation of multi-agent service plays an important role in enterprises. This thesis mainly studies the concept of the Multi-agent Participate Model of Service Innovation and explores the strategic role and position of the model in communication industry. The purpose of this thesis includes two parts: firstly it studies the service innovation which multi-agent such as enterprises, customers and suppliers participate in so as to establish a new theoretical framework of such service innovation. Secondly, from the perspective of strategic management of enterprises, it considers the selection of Multi-agent Participate Model of Service Innovation in competitive strategies of the communication industry to clarify the strategic role and position of such model in management of the communication industry. Questionnaire and interview are two main data acquisition methods in this thesis. The author surveyed 100 employees from China Telecom with a questionnaire designed by herself. The data shows that customers’ demands for market and competitors’ competition in the market have a great influence on the innovation activities of enterprises. Some senior managers of China Telecom have been interviewed for this study. The interviews have shown the significance of Multi-agent Participate Model of Service Innovation in telecommunication industry and the Multi-agent Participate Model of Service Innovation can favorably be applied to telecommunication industry. The enterprises, customers, employees, managers and suppliers are of inseparable relationships in the model. As an innovation model of enterprises, Multi-agent Participate Model of Service Innovation can better mobilize enthusiasm of each participant of service innovation, and innovativeness of management service to clarify the strategic position of the model in enterprise management.