Component-based software architectures and multi-agent systems: mutual and complementary contributions for supporting software development

Dans cette thèse, nous explorons les diverses contributions que les systèmes multi-agents (SMA) et les architectures à base de composants (CBSA) peuvent mutuellement et complémentairement s'apporter l'un à l'autre. Dans un premier temps, nous définissons, illustrons, analysons et discutons une méthodologie du développement des SMA, un modèle de composants (SpeAD), un langage de description d'architecture (SpeADL) et une méthode de conception (SpEArAF) qui facilitent et guident la description et l'implémentation des SMA. Cette réponse complète au développement des SMA est assistée par un outil (MAY) et a été appliquée à un grand nombre d'applications. Dans un second temps, nous explorons à travers divers expériences l'aide que peuvent apporter les SMA auto-adaptatif aux CBSA. Les agents et leur réorganisation continuelle jouent à la fois le rôle de moteur de la construction et de l'adaptation dynamique de l'architecture, mais aussi du conteneur qui connecte ses éléments en pratique.In this thesis, we explore the various aspects of the mutual and complementary contributions that multi-agent systems (MASs) and component-based software architectures (CBSAs) can provide to each other. On one hand, we define, illustrate, analyse and discuss an architecture-oriented methodology of MAS development, a component model (SpeAD), an architectural description language (SpeADL) and a design method (SpEArAF) that ease and guide the description and the implementation of MASs. This complete answer to MAS development is supported by a tool (MAY) and has been applied to many applications. On the other hand, we explore through various experiments how self-adaptive MASs can be used to support CBSAs. The agents and their continuous reorganisation act both as the engine of the construction and of the dynamic adaptation of the architecture, and as the runtime container that practically connects its elements together

Energy Efficiency

Energy efficiency is finally a common sense term. Nowadays almost everyone knows that using energy more efficiently saves money, reduces the emissions of greenhouse gasses and lowers dependence on imported fossil fuels. We are living in a fossil age at the peak of its strength. Competition for securing resources for fuelling economic development is increasing, price of fuels will increase while availability of would gradually decline. Small nations will be first to suffer if caught unprepared in the midst of the struggle for resources among the large players. Here it is where energy efficiency has a potential to lead toward the natural next step - transition away from imported fossil fuels! Someone said that the only thing more harmful then fossil fuel is fossilized thinking. It is our sincere hope that some of chapters in this book will influence you to take a fresh look at the transition to low carbon economy and the role that energy efficiency can play in that process

Sistema multi-agente basado en contexto, localización y reputación para dominios de inteligencia artificial

Las investigaciones en Inteligencia Ambiental (denominada también Computación Ubicua) utilizando tecnologías inalámbricas han crecido en los últimos años a pasos agigantados. El término Inteligencia Ambiental es un nuevo concepto adoptado para hacer referencia a entornos inteligentes, en donde la computadora y los dispositivos móviles asisten al usuario en sus actividades cotidianas. Son entornos con un gran despliegue de diferentes tecnologías, invisibles al usuario, que hacen posible la provisión de servicios personalizados. Los agentes inteligentes son un paradigma especial dentro de la Inteligencia Ambiental. La principal contribución de este trabajo consiste en el diseño metodológico de un sistema basado en contexto (incluyendo servicios de localización) utilizando agentes inteligentes, que da soporte a las necesidades de información de estas nuevas tecnologías, para dominios heterogéneos. Los agentes facilitan la comunicación y las interacciones entre los usuarios del sistema. Para hacer factible este intercambio de mensajes, es necesario contar con una ontología, la cual posibilita, no tan solo la comunicación entre los agentes intervinientes, sino que además, permite a los agentes razonar sobre el contexto en el que se sitúan. En este sentido, el uso de información geográfica permite inferir información de alto nivel sobre dónde se encuentra un agente. Las preferencias de los usuarios permiten seleccionar los mejores servicios en cada escenario de Inteligencia Ambiental. Esta personalización es particularmente adecuada en los sistemas multiagentes con capacidades de aprendizaje. En base a esta concepción, se aborda la adquisición de perfiles de usuarios, de forma no intrusiva, a través de técnicas de computación evolutiva. La adquisición de las preferencias del usuario hacen posible que se le presente al usuario una lista de actividades priorizada por realizar, según el dominio en el que se encuentre a efectos de maximizar factores que le sean relevantes. Además, en base a estos perfiles, los agentes proveedores realizan sus ofertas. Finalmente, las interacciones usuarios-proveedores en un dominio dinámico, requieren de cierto grado de confianza, el cual se adquiere a través del uso de sistemas de confianza y reputación. En este sentido, se presenta CALoR, un modelo de reputación basado en contexto y localización, que permite a los agentes usuarios del sistema realizar recomendaciones a otros agentes usuarios, considerando sus experiencias pasadas, y también aspectos espacio-temporales. Se asume que la recomendación enviada por un agente usuario sobre un agente proveedor es más fiable cuando han interactuado recientemente, y el agente usuario ha podido evaluar el servicio provisto a una distancia considerablemente corta. Resumiendo, en las páginas siguientes, se presentará al lector, una arquitectura multi-agente basada en contexto y localización para dominios heterogéneos de inteligencia ambiental. Se explicará en detalle la construcción del perfil de usuario utilizando algoritmos genéticos para la provisión de servicios personalizados, y se presentará el modelo de reputación CALoR para afianzar las relaciones entre los agentes de la arquitectura. Por cada módulo, se presentan los experimentos realizados y los resultados obtenidos, que demuestran la robustez de la propuesta global. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Researches on Ambient Intelligent (also known as Ubiquitous Computing) using wireless technologies have increased over the last few years. Ambient Intelligence is a new concept adopted for referencing an intelligent environment, where the user is assist by the computer and his mobile device wherever he is. Several technologies are necessaries in these environments to provide personalized services. All immersed devices are invisibles to the user. Ambient Intelligence is an ideal workspace for agents because of autonomous distributed and proactive agent nature. The main contribution of this Thesis consists of designing a methodological approach a Context Aware System (involving location services) using Agents that can be used in very different domains. We review several scenarios to define a multi-agent architecture, that support the information needs of these new technologies, that will be used in heterogeneous domain. We use ontology to facilitate the communication between agents. Furthermore, this ontology allows agent’s reasoning. In this manner, the use of geographic information allows to infer high level information about the real place where the agent is. In Ambient Intelligence scenarios, user preferences allow to select the best services for each user. This personalization is particularly suitable in a multi-agent system with learning capabilities. Based on this, and on the improvement of the mobile technologies, we developed an algorithm that allows learning the user profile. A list of prioritized services would therefore be presented to the user that takes into account user interests. Also, the providers agents could make their offers. Finally, users-providers interactions that take place in a dynamical domain needs a certain trust. Trust is build using trust and reputation systems. In this manner, we present CALoR reputation model that is a reputation model based-on context and location. This model allows the user agents of the systems to make recommendations from each other. CALoR take into account the past interactions of the agents and their spatial-temporal issues. We assume that a recommendation send by a user agent over a provider agent is more reliable when the agents interact closely and recently. In summary, the reader will found in next pages a multi-agent system based-on context and location for heterogeneous domain in ambient intelligence. We explain the detail of the user profile acquisition using genetic algorithms. And we present CALoR system to consolidate the relationship between agents. For each item, we expose our experiments and the obtained results, that validates this approach

International Navigation Market

Economic record of human society in the last period has involved an unprecedented growth of world trade, trafficking of basic raw materials needed for industry and agriculture, and trade in industrial products or food. To the huge volume of movement of goods, shipping takes back the role of first order both quantitatively as well as efficiency. This situation is encouraged by factors such as diversification of trade, number of participants in this process and the increasingly complex international trade

International Navigation Market

Economic record of human society in the last period has involved an unprecedented growth of world trade, trafficking of basic raw materials needed for industry and agriculture, and trade in industrial products or food. To the huge volume of movement of goods, shipping takes back the role of first order both quantitatively as well as efficiency. This situation is encouraged by factors such as diversification of trade, number of participants in this process and the increasingly complex international trade