e-Learning is becoming an influential role as an economic method and a flexible mode of study in the institutions of higher education today which has a presence in an increasing number of college and university courses. e-Learning as system of systems is a dynamic and scalable environment. Within this environment, e-learning is still searching for a permanent, comfortable and serviceable position that is to be controlled, managed, flexible, accessible and continually up-to-date with the wider university structure. As most academic and business institutions and training centres around the world have adopted the e-learning concept and technology in order to create, deliver and manage their learning materials through the web, it has become the focus of investigation. However, management, monitoring and collaboration between these institutions and centres are limited. Existing technologies such as grid, web services and agents are promising better results. In this research a new architecture has been developed and adopted to make the e-learning environment more dynamic and scalable by dividing it into regional data grids which are managed and monitored by agents. Multi-agent technology has been applied to integrate each regional data grid with others in order to produce an architecture which is more scalable, reliable, and efficient. The result we refer to as Regionally Distributed Architecture for Dynamic e-Learning Environment (RDADeLE). Our RDADeLE architecture is an agent-based grid environment which is composed of components such as learners, staff, nodes, regional grids, grid services and Learning Objects (LOs). These components are built and organised as a multi-agent system (MAS) using the Java Agent Development (JADE) platform. The main role of the agents in our architecture is to control and monitor grid components in order to build an adaptable, extensible, and flexible grid-based e-learning system. Two techniques have been developed and adopted in the architecture to build LOs' information and grid services. The first technique is the XML-based Registries Technique (XRT). In this technique LOs' information is built using XML registries to be discovered by the learners. The registries are written in Dublin Core Metadata Initiative (DCMI) format. The second technique is the Registered-based Services Technique (RST). In this technique the services are grid services which are built using agents. The services are registered with the Directory Facilitator (DF) of a JADE platform in order to be discovered by all other components. All components of the RDADeLE system, including grid service, are built as a multi-agent system (MAS). Each regional grid in the first technique has only its own registry, whereas in the second technique the grid services of all regional grids have to be registered with the DF. We have evaluated the RDADeLE system guided by both techniques by building a simulation of the prototype. The prototype has a main interface which consists of the name of the system (RDADeLE) and a specification table which includes Number of Regional Grids, Number of Nodes, Maximum Number of Learners connected to each node, and Number of Grid Services to be filled by the administrator of the RDADeLE system in order to create the prototype. Using the RST technique shows that the RDADeLE system can be built with more regional grids with less memory consumption. Moreover, using the RST technique shows that more grid services can be registered in the RDADeLE system with a lower average search time and the search performance is increased compared with the XRT technique. Finally, using one or both techniques, the XRT or the RST, in the prototype does not affect the reliability of the RDADeLE system.

In recent years, there has been a flood of continuously changing information from a variety of web resources such as web databases, web sites, web services and programs. Online Social Networks (OSNs) represent such a field where huge amounts of information are being posted online over time. Due to the nature of OSNs, which offer a productive source for qualitative and quantitative personal information, researchers from various disciplines contribute to developing methods for extracting data from OSNs. However, there is limited research which addresses extracting data automatically. To the best of the author's knowledge, there is no research which focuses on tracking the real time changes of information retrieved from OSN profiles over time and this motivated the present work. This thesis presents different approaches for automated Data Extraction (DE) from OSN: crawler, parser, Multi Agent System (MAS) and Application Programming Interface (API). Initially, a parser was implemented as a centralized system to traverse the OSN graph and extract the profile's attributes and list of friends from Myspace, the top OSN at that time, by parsing the Myspace profiles and extracting the relevant tokens from the parsed HTML source files. A Breadth First Search (BFS) algorithm was used to travel across the generated OSN friendship graph in order to select the next profile for parsing. The approach was implemented and tested on two types of friends: top friends and all friends. In case of top friends, 500 seed profiles have been visited; 298 public profiles were parsed to get 2197 top friends' profiles and 2747 friendship edges, while in case of all friends, 250 public profiles have been parsed to extract 10,196 friends' profiles and 17,223 friendship edges. This approach has two main limitations. The system is designed as a centralized system that controlled and retrieved information of each user's profile just once. This means that the extraction process will stop if the system fails to process one of the profiles; either the seed profile (first profile to be crawled) or its friends. To overcome this problem, an Online Social Network Retrieval System (OSNRS) is proposed to decentralize the DE process from OSN through using MAS. The novelty of OSNRS is its ability to monitor profiles continuously over time. The second challenge is that the parser had to be modified to cope with changes in the profiles' structure. To overcome this problem, the proposed OSNRS is improved through use of an API tool to enable OSNRS agents to obtain the required fields of an OSN profile despite modifications in the representation of the profile's source web pages. The experimental work shows that using API and MAS simplifies and speeds up the process of tracking a profile's history. It also helps security personnel, parents, guardians, social workers and marketers in understanding the dynamic behaviour of OSN users. This thesis proposes solutions for web database processing on data extraction from OSNs by the use of parser and MAS and discusses the limitations and improvements.

Le travail de recherche porte sur deux thèmes: 1. développement d'un modèle dynamique multimodal basé sur les activités ; 2. développement de méthodes de résolution de l'équilibre du réseau multimodal dynamique. Nous proposons un modèle dynamique multimodal simulant l'enchaînement des déplacements basé sur les activités. Deux nouvelles méthodes de résolution du problème d'équilibre du réseau sont proposées : 1. l'algorithme de colonie de fourmis en discrétisation temporelle ; 2. la méthode de l'Entropie Relative. Cette dernière constitue une avancée méthodologique qui considère que l'équilibre du réseau est un événement rare parmi les états possibles du réseau. Pour réduire les besoins en moyens de calcul pour l'application dans un grand réseau, nous simulons les voyageurs par paquets en nous appuyant sur un nouveau modèle macroscopique de trafic en coordonnées Lagrangiennnes. Ce modèle permet de décrire la propagation des paquets conformément au modèle macroscopique du premier ordre.

Ce travail de thèse adresse une problématique industrielle qui s'articule autour de la modélisation de la chaîne logistique, composée essentiellement de groupements de PME mécatroniques, afin d'analyser les caractéristiques organisationnelles et comportementales et d'en évaluer la performance. Le développement de la solution de modélisation s'inscrit dans une démarche de raffinement itératif et d'intégration progressive de modèles. Le cadre de cette démarche constitue le fondement du processus de développement adopté « ArchMDE » basé sur l'Ingénierie Dirigée par les Modèles (IDM). Par conséquent, les travaux de cette recherche sont caractérisés par une contribution double. Dans la première contribution, il s'agit de la proposition d'un métamodèle générique de la chaîne logistique. En puisant dans la littérature nous avons proposé une méthodologie de conceptualisation incrémentale et générique qui permet la génération d'un modèle conceptuel de la structure d'une chaîne logistique ainsi que les relations entre ses différentes entités. L'application de cette démarche a donné lieu à la construction d'un métamodèle décrivant le champ d'application étudié (PME mécatroniques). La seconde contribution permet de formaliser le comportement dynamique des concepts générés lors de la première étape en adoptant le paradigme multi-agent. Ainsi, nous avons proposé une agentification du métamodèle du domaine en se basant sur l'analogie naturelle entre la chaîne logistique et le système multi-agent. Cette agentification a donné lieu à un métamodèle du domaine agentifié avec une mise en place des protocoles d'interaction visant à synchroniser et piloter la chaîne logistique. L'étude des relations et du comportement dynamique des différents concepts a permis d'aboutir à un métamodèle d'implémentation qui exprime à la fois les deux vues statique et dynamique du domaine d'application. Ce dernier artefact a fait l'objet d'une instanciation et d'une implémentation d'un cas industriel réel dans un environnement JAVA.

Dans cette thèse, nous proposons des modèles logiques pour la représentation des croyances et leur changement dans un cadre multi-agent, en insistant sur l'importance de se fixer un point de vue particulier pour la modélisation. A cet égard, nous distinguons deux approches différentes: l'approche externe, où le modélisateur est quelqu'un d'externe à la situation; l'approche interne, où le modélisateur est l'un des agents. Nous proposons une version interne de la logique épistémique dynamique (avec des modèles d'événements), ce qui nous permet de généraliser facilement la théorie de la révision des croyances d'AGM au cas multi-agent. Ensuite, nous mod´elisons les dynamismes logiques complexes qui soustendent notre interprétation des événements en introduisant des probabilités et des infinitésimaux. Finalement, nous proposons un formalisme alternatif qui n'utilise pas de modèle d'événement mais qui introduit à la place un opérateur d'événement inverse.

Nous construisons un simulateur de marche financier multi-agent. Dans cette marche l'échange des actions n'est pas fait en continu. Le prix de marché est formé à l'aide d'un carnet d'ordres. Les investisseurs que l'on modélise reçoivent de l'information biaisée et ils essayent de maximiser leur richesse. Les différents types d'investisseurs, comme les bruiteurs, chartistes ou informées, coexistent dans notre marche. On montre comment les faites stylises peuvent être causée par la présence des investisseurs chartistes ou par des simple délais dans l'information. Nous montrons comment les bulles de prix sont possibles dans un marché avec des investisseurs bien informés. On découvre que c'est profitable, pour un investisseur informé, d'adopter dans certaines moments des stratégies techniques. A partir de nos résultats nous proposons une nouvelle théorie sur la dynamique des marchés financiers, appelé " marches parfois efficientes ".

We present a motion control framework which allows a group of robots to work together to decide upon their motions by minimizing a collective cost without any central computing component or any one agent performing a large portion of the computation. When developing distributed control algorithms, care must be taken to respect the limited computational capacity of each agent as well as respect the information and communication constraints of the network. To address these issues, we develop a distributed, behavior-based model predictive control (MPC) framework which alleviates the computational difficulties present in many distributed MPC frameworks, while respecting the communication and information constraints of the network. In developing the multi-agent control framework, we make three contributions. First, we develop a distributed optimization technique which respects the dynamic communication restraints of the network, converges to a collective minimum of the cost, and has transients suitable for robot motion control. Second, we develop a behavior-based MPC framework to control the motion of a single-agent and apply the framework to robot navigation. The third contribution is to combine the concepts of distributed optimization and behavior-based MPC to develop the mentioned multi-agent behavior-based MPC algorithm suitable for multi-robot motion control.

The High Redundancy Actuator (HRA) project investigates a novel approach to fault tolerant actuation, which uses a high number of small actuation elements, assembled in series and parallel in order to form a single intrinsically fault tolerant actuator. Element faults affect the maximum capability of the overall actuator, but through control techniques, the required performance can be maintained. This allows higher levels of reliability to be attained in exchange for less over-dimensioning in comparison to conventional redundancy techniques. In addition, the combination of both serial and parallel elements provides intrinsic accommodation of both lock-up and loose faults. Research to date has concentrated on HRAs based on electromechanical technology, of relatively low order, controlled through passive Fault Tolerant Control (FTC) methods. The objective of this thesis is to expand upon this work. HRA configurations of higher order, formed from electromagnetic actuators are considered. An element model for a moving coil actuator is derived from first principles and verified experimentally. This element model is then used to form high-order, non-linear HRA models for simulation, and reduced-order representations for control design. A simple, passive FTC law is designed for the HRA configurations, the results of which are compared to a decentralised, active FTC approach applied through a framework based upon multi-agent concepts. The results indicate that limited fault tolerance can be achieved through simple passive control, however, performance degradation occurs, and requirements are not met under theoretically tolerable fault levels. Active FTC offers substantial performance improvements, meeting the requirements of the system under the vast majority of theoretically tolerable fault scenarios. However, these improvements are made at the cost of increased system complexity and a reliance on fault detection. Fault Detection (FD) and health monitoring of the HRA is explored. A simple rule-based FD method, for use within the active FTC, is described and simulated. An interacting multiple model FD method is also examined, which is more suitable for health monitoring in a centralised control scheme. Both of these methods provide the required level of fault information for their respective purposes. However, they achieve this through the introduction of complexity. The rule-based method increases system complexity, requiring high levels of instrumentation, and conversely the interacting multiple model approach involves complexity of design and computation. Finally, the development of a software demonstrator is described. Experimental rigs at the current project phase are restricted to relatively low numbers of elements for practical reasons such as cost, space and technological limitations. Hence, a software demonstrator has been developed in Matlab/Simulink which provides a visual representation of HRAs with larger numbers of elements, and varied configuration for further demonstration of this concept.

A "complex" system typically has a relatively large number of dynamically interacting components and tends to exhibit emergent behavior that cannot be explained by analyzing each component separately. A biological neural network is one example of such a system. A multi-agent model of such a network is developed to study the relationships between a network's structure and its spike train output. Using this model, inferences are made about the synaptic structure of networks through cluster analysis of spike train summary statistics A complexity measure for the network structure is also presented which has a one-to-one correspondence with the standard time series complexity measure sample entropy.

The work presented herein focuses on the design of distributed autonomous controllers for collective behaviour of Micro-unmanned Aerial Vehicles (MAVs). Two alternative approaches to this topic are introduced: one based upon the Evolutionary Robotics (ER) paradigm, the other one upon flocking principles. Three computer simulators have been developed in order to carry out the required experiments, all of them having their focus on the modelling of fixed-wing aircraft flight dynamics. The employment of fixed-wing aircraft rather than the omni-directional robots typically employed in collective robotics significantly increases the complexity of the challenges that an autonomous controller has to face. This is mostly due to the strict motion constraints associated with fixed-wing platforms, that require a high degree of accuracy by the controller. Concerning the ER approach, the experimental setups elaborated have resulted in controllers that have been evolved in simulation with the following capabilities: (1) navigation across unknown environments, (2) obstacle avoidance, (3) tracking of a moving target, and (4) execution of cooperative and coordinated behaviours based on implicit communication strategies. The design methodology based upon flocking principles has involved tests on computer simulations and subsequent experimentation on real-world robotic platforms. A customised implementation of Reynolds’ flocking algorithm has been developed and successfully validated through flight tests performed with the swinglet MAV. It has been notably demonstrated how the Evolutionary Robotics approach could be successfully extended to the domain of fixed-wing aerial robotics, which has never received a great deal of attention in the past. The investigations performed have also shown that complex and real physics-based computer simulators are not a compulsory requirement when approaching the domain of aerial robotics, as long as proper autopilot systems (taking care of the ”reality gap” issue) are used on the real robots.