Psychological studies have shown that information exchange is a key component of effective teamwork. In addition to requesting information that they need for their tasks, members of effective teams often proactively forward information that they believe other teammates require to complete their tasks. We refer to this type of communication as proactive information exchange and the formalization and implementation of this is the subject of this thesis. The important question that we are trying to answer is: under normative conditions, what types of information needs can agent teammates extract from shared plans and how can they use these information needs to proactively forward information to teammates? In the following, we make two key claims about proactive information exchange: first, agents need to be aware of the information needs of their teammates and that these information needs can be inferred from shared plans; second, agents need to be able to model the beliefs of others in order to deliver this information efficiently. To demonstrate this, we have developed an algorithm named PIEX, which, for each agent on a team, reasonably approximates the information-needs of other team members, based on analysis of a shared team plan. This algorithm transforms a team plan into an individual plan by inserting coomunicative tasks in agents' individual plans to deliver information to those agents who need it. We will incorporate a previously developed architecture for multi-agent belief reasoning. In addition to this algorithm for proactive information exchange, we have developed a formal framework to both describe scenarios in which proactive information exchange takes place and to evaluate the quality of the communication events that agents running the PIEX algorithm generate. The contributions of this work are a formal and implemented algorithm for information exchange for maintaining a shared mental model and a framework for evaluating domains in which this type of information exchange is useful.

All-electric ship power systems have limited generation capacity and finite rotating inertia compared with large power systems. Moreover, all-electric ship power systems include large portions of nonlinear loads and dynamic loads relative to the total power capacity, which may significantly reduce the stability margin. Pulse loads and other high-energy weapon loads in the system draw a large amount of power intermittently, which may cause significant frequency and voltage oscillations in the system. Thus, an effective real-time load management technique is needed to dynamically balance the load and generation to operate the system normally. Multi-agent systems, inspired by biological phenomena, aim to cooperatively achieve system objectives that are difficult to reach by a single agent or centralized controller. Since power systems include various electrical components with different dynamical systems, conventional homogeneous multi-agent system cooperative controllers have difficulties solving the real-time load management problem with heterogeneous agents. In this dissertation, a novel heterogeneous multi-agent system cooperative control methodology is presented based on artificial potential functions and reduced-order agent models to cooperatively achieve real-time load management for all-electric ship power systems. The technique integrates high-order system dynamics and various kinds of operational constraints into the multi-agent system, which improves the accuracy of the cooperative controller. The multi-agent system includes a MVAC multiagent system and a DC zone multi-agent, which are coordinated by an AC-DC communication agent. The developed multi-agent system framework and the notional all-electric ship power system model were simulated in PSCAD software. Case studies and performance analysis of the MVAC multi-agent system and the DC zone multi-agent system were performed. The simulation results indicated that propulsion loads and pulse loads can be successfully coordinated to reduce the impact of pulse loads on the power quality of all-electric ship power systems. Further, the switch status or power set-point of loads in DC zones can be optimally determined to dynamically balance the generation and load while satisfying the operational constraints of the system and considering load priorities. The method has great potential to be extended to other isolated power systems, such as microgrids.

Energy is consumed every day at home as we perform simple tasks, such as watching television, washing dishes and heating/cooling home spaces during season of extreme weather conditions, using appliances, or turning on lights. Most often, the energy resources used in residential systems are obtained from natural gas, coal and oil. Moreover, climate change has increased awareness of a need for expendable, energy resources. As a result, carbon dioxide emissions are increasing and creating a negative effect on our environment and on our health. In fact, growing energy demands and limited natural resource might have negative impacts on our future. Therefore, saving energy is becoming an important issue in our society and it is receiving more attention from the research community. This thesis introduces a intelligent energy controller algorithm based on software agent approach that reduce the energy consumption at home for both heating and cooling spaces by considering the user’s occupancy, outdoor temperature and user’s preferences as input to the system. Thus the proposed approach takes into consideration the occupant’s preferred temperature, the occupied and unoccupied spaces, as well as the time spent in each area of the home. A Java based simulator has been implemented to simulate the algorithm for saving energy in heating and cooling systems. The results from the simulator are compared to the results of using HOT2000, which is Canada’s leading residential energy analysis and rating software developed by CanmetENERGY’s Housing, Buildings, Communities and Simulation (HBCS) group. We have calculated how much energy a home modelled will use under emulated conditions. The results showed that the implementation of the proposed energy controller algorithm can save up to 50% in energy consumption in homes dedicated to heating and cooling systems compared to the results obtained by using HOT2000.

La conception d'applications pour un environnement attentif (applications attentives) soulève de nombreux défis, en particulier liés à l'hétérogénéité des objets communicants, à l'instabilité de l'environnement et à la variabilité des besoins. Afin de faciliter cette conception, on s'intéresse à la notion d'application composite flexible : des applications qui fonctionnent en continu, en arrière plan de l'attention, et agrègent des fonctionnalités fournies par les objets communicants, afin d'accompagner l'interaction des utilisateurs. FCAP est un intergiciel pour la composition flexible d'applications, qui fournit un support générique pour intégrer les fonctionnalités, s'adapter à un environnement instable et ajuster l'équilibre entre l'automatisation et le contrôle par les utilisateurs. Cet intergiciel développe des principes issus de l'architecture orientée-service, du Web sémantique et des systèmes multi-agents, particulièrement pertinentes pour les environnements considérés.

Au sein des entreprises, un composant vital dans la prise de décision, qu'elle soit globale ou locale, est le système d'information. Celui-ci contient, en effet, les informations nécessaires pour décider, agir, apprendre, comprendre, prévoir et contrôler. Sa structure est généralement liée à l'histoire de l'entreprise dans le sens où cet ensemble s'est constitué d'éléments qui se sont juxtaposés au fil du temps au gré des choix stratégiques formant in fine un ensemble complexe et hétérogène. L'existence de plusieurs systèmes d'information, au sein de grande sociétés, pouvant rendre la recherche d'information difficile pour les utilisateurs métiers. 'objectif de nos travaux consiste à permettre aux utilisateurs d'explorer les connaissances, formulées en terme métier, contenues dans plusieurs systèmes d'informations et de récupérer les données qui leur sont associées. Il s'agit donc de mettre à disposition des utilisateurs une vue globale des connaissances disponibles liées à leurs domaines métiers, tout en dissimulant la diversité des sources d'informations et en garantissant que les données associées sont récupérables malgré les changements qui peuvent se produire au sein des différents systèmes. ans cette thèse nous proposons KRISMAS, une solution d'intégration de données basée une représentation des connaissances métiers et une architecture Multi-Agents. La représentation des connaissances prend la forme d'une ou plusieurs ontologies de domaine permettant aux utilisateurs d'explorer les connaissances des sources de données et de formuler des requêtes pour la récupération des données. Les agents sont utilisés pour l'exploitation de la représentation des connaissances métiers ainsi que pour la gestion des changements au sein d'un système d'intégration

Nous défendons la thèse selon laquelle la modélisation des Algorithmes Génétiques Coévolutionnaires (AGCs) sous forme de systèmes multi-agent organisationnels répond au manque d'expressivité en termes de structure, d'interactions et d'adaptation de ces algorithmes dans les modèles et plateformes existants. Dans cette optique nous introduisons MAS4EVO, Multi-Agent Systems for EVolutionary Optimization, un nouveau modèle agent (re-)organisationnel basé sur Moise+. MAS4EVO est implémenté dans DAFO (Distributed Agent Framework for Optimization), un framework multi-agent organisationnel permettant l'utilisation, la manipulation et la distribution d'AGCs existants et nouvellement créés (hybride et dynamique) pour l'optimisation de problèmes difficiles. Les expérimentations de ces AGCs ont été conduites sur deux problèmes d'optimisation métier, le premier étant un problème de gestion de stock et le second étant un problème de contrôle de topologie dans les réseaux ad hoc sans fil.

In this thesis, a solution to the multi-Unmanned Aerial Vehicle (UAV) search and intercept problem for a moving target is presented. For the search phase, an adapted diffusion-based algorithm is used to manage the target uncertainty while individual UAVs are controlled with a hybrid receding horizon / potential method. The coordinated search is made possible by an uncertainty weighting process. The team intercept phase algorithm is a behavioural approach based on the analytical solution of Isaac's Single-Pursuer/Single-Evader (SPSE) homicidal chau ffeur problem. In this formulation, the intercepting control is taken to be a linear combination of the individual SPSE controls that would exist for each of the evader/pursuer pairs. A particle swarm optimizer is applied to find approximate optimal weighting coefficients for discretized intervals of the game time. Simulations for the team search, team intercept and combined search and intercept problem are presented.

In this thesis, a solution to the multi-Unmanned Aerial Vehicle (UAV) search and intercept problem for a moving target is presented. For the search phase, an adapted diffusion-based algorithm is used to manage the target uncertainty while individual UAVs are controlled with a hybrid receding horizon / potential method. The coordinated search is made possible by an uncertainty weighting process. The team intercept phase algorithm is a behavioural approach based on the analytical solution of Isaac's Single-Pursuer/Single-Evader (SPSE) homicidal chau ffeur problem. In this formulation, the intercepting control is taken to be a linear combination of the individual SPSE controls that would exist for each of the evader/pursuer pairs. A particle swarm optimizer is applied to find approximate optimal weighting coefficients for discretized intervals of the game time. Simulations for the team search, team intercept and combined search and intercept problem are presented.

Energy is consumed every day at home as we perform simple tasks, such as watching television, washing dishes and heating/cooling home spaces during season of extreme weather conditions, using appliances, or turning on lights. Most often, the energy resources used in residential systems are obtained from natural gas, coal and oil. Moreover, climate change has increased awareness of a need for expendable, energy resources. As a result, carbon dioxide emissions are increasing and creating a negative effect on our environment and on our health. In fact, growing energy demands and limited natural resource might have negative impacts on our future. Therefore, saving energy is becoming an important issue in our society and it is receiving more attention from the research community. This thesis introduces a intelligent energy controller algorithm based on software agent approach that reduce the energy consumption at home for both heating and cooling spaces by considering the user’s occupancy, outdoor temperature and user’s preferences as input to the system. Thus the proposed approach takes into consideration the occupant’s preferred temperature, the occupied and unoccupied spaces, as well as the time spent in each area of the home. A Java based simulator has been implemented to simulate the algorithm for saving energy in heating and cooling systems. The results from the simulator are compared to the results of using HOT2000, which is Canada’s leading residential energy analysis and rating software developed by CanmetENERGY’s Housing, Buildings, Communities and Simulation (HBCS) group. We have calculated how much energy a home modelled will use under emulated conditions. The results showed that the implementation of the proposed energy controller algorithm can save up to 50% in energy consumption in homes dedicated to heating and cooling systems compared to the results obtained by using HOT2000.

Les travaux présentés ici, dans le cadre des Systèmes Multi-Agents (SMA) et de l'Intelligence Artificielle Distribuée (IAD), s'intéressent au problème de l'organisation dans les "systèmes ouverts". Dans ce cadre, nos recherches visent à définir des modèles de comportement sociaux permettant aux agents de s'organiser pour s'adapter à leur environnement. Notre démarche fait appel, entre autres principes méthodologiques, au concept de "cercle vertueux" qui se propose d'emprunter des métaphores à d'autres disciplines scientifiques pour concevoir des modèles informatiques. Dans un premier temps, nous recherchons dans les sociétés animales (en l'occurrence chez les primates) des métaphores de comportements proches de nos besoins. Nous définissons alors des modèles d'agent qui permettent, en simulation, de reproduire une des caractéristiques sociales observées chez les primates, la reconnaissance des relations de dominance. Ces modèles font ensuite l'objet d'une transposition à un domaine différent, la construction collective d'un lexique, pour estimer plus finement les dynamiques collectives sous-jacentes. Dans un second temps, nous nous intéressons à une expérimentation de "Robotique Collective Ouverte", dans laquelle un groupe de robots doit s'adapter à un environnement où travaillent des humains (projet MICRobES). Nous montrons que dans ces conditions, une simple transposition n'est plus possible et qu'il faut prendre en compte la corporéité des robots. Nous proposons alors pour cela des principes de conception de comportements d'agents faisant appel à la sélection naturelle (l'Ethogénétique) et nous présentons les résultats obtenus avec un framework implémentant ces concepts (ATNoSFERES). Nous montrons ainsi comment élargir les principes de départ en conciliant approche multi-agent et algorithmes évolutionnistes, en empruntant des concepts issus de l'éthologie.