<p dir="ltr">Unmanned Aerial Vehicles (UAVs) are increasingly applied in agricultural tasks such as crop monitoring, especially with AI-driven enhancements significantly increasing their autonomy and ability to execute complex operations without human interventions. However, existing UAV systems lack efficiency, intuitive user interfaces using natural language processing for command input, and robust security which is essential for real-time operations in dynamic environments. In this paper, we propose a novel solution to create a secure, efficient, and user-friendly interface for UAV control by integrating Large Language Model (LLM) with the case study on agricultural environment. In particular, we designed a four-stage approach that allows only authorized user to issue voice commands to the UAV. The command is issued to the LLM controller processed by LLM using API and generates UAV control code. Additionally, we focus on optimizing UAV battery life and enhancing scene interpretation of the environment. We evaluate our approach using AirSim and an agricultural setting built in Unreal Engine, testing under various conditions, including variable weather and wind factors. Our experimental results confirm our method's effectiveness, demonstrating improved operational efficiency and adaptability in diverse agricultural scenarios.</p>

Les systèmes complexes, naturels et artificiels, ont reçu récemment une attention renouvelée : les systèmes naturels, notamment biologiques, du fait de la nécessité de les appréhender dans une démarche systémique ; les systèmes artificiels, du fait de la dématérialisation de l'ordinateur amorcée avec l'informatique ubiquitaire. L'art, de son côté, explore depuis toujours le détournement des dernières avancées scientifiques et technologiques pour la création d'oeuvres singulières. Le travail mené depuis dix ans se situe à la croisée de ces chemins, dans le cadre unificateur des systèmes multi-agents. Je me suis intéressé plus particulièrement à l'interaction homme-machine dans le contexte de l'informatique ambiante, dans l'idée d'une construction automatique et d'une régulation dynamique de systèmes d'interaction. Ce travail est alimenté par la recherche menée dans le cadre de la simulation à base d'agents, aussi bien du point de vue des concepts et outils développés, que du point de vue de l'inspiration tirée des mécanismes d'auto-organisation et de régulation des systèmes étudiés. L'art fournit quant à lui un cadre expérimental original par la mise en scène métaphorique, dans des performances numériques interactives, des situations étudiées.

Cette thèse s'inscrit dans le cadre d'un projet de recherche et de développement qui vise à mettre en place des technologies de simulation permettant de reproduire des comportements humains dans une ville. L'objectif de nos travaux est de définir des algorithmes permettant de simuler les déplacements d'une grande quantité de piétons dans un environnement urbain, en temps réel, et de manière crédible. Pour ce type d'exercice, plusieurs solutions existent. Ces solutions sont principalement développées à partir de deux types d'approches : les approches microscopiques, où les piétons sont modélisés comme des agents autonomes, et les approches macroscopiques, où les piétons sont considérés comme soumis à des lois d'écoulement continues ou discrètes. Notre position est que ces deux approches ne s'opposent pas, contrairement à ce qui ressort de la pratique courante, mais se complètent mutuellement. Privilégier l'une au détriment de l'autre fait courir le risque de produire des solutions partiellement satisfaisantes. Aussi nous sommes nous proposés de clarifier le cadre formel permettant d'appréhender la complexité des déplacements. En ligne avec plusieurs études statistiques et psychologiques sur le déplacement des piétons, nous explicitons un déplacement crédible comme un déplacement économe en énergie métabolique. Nous nous inspirons des jeux de congestion et du paradigme multi-agent pour proposer une formulation générique du problème de déplacement des piétons : nous introduisons la notion de ressources de navigation, que nous décrivons comme des régions de l'espace que les agents utilisent pour atteindre leurs destinations, et via lesquelles les agents interagissent pour estimer leurs dépenses énergétiques de manière robuste. Nous proposons une stratégie de déplacement basée sur les heuristiques taboues et nous considérons le principe influence et réaction pour implémenter les actions de déplacements. Le concept d'environnement issu du paradigme multi-agent s'avère particulièrement utile pour appréhender la complexité de la simulation. L'environnement est considéré comme un composant indépendant et ontologiquement différent des agents qui est pris en compte à tous les niveaux de décisions. Une importante partie de la dynamique de la simulation peut ainsi être déléguée à l'environnement sans altérer l'autonomie des agents. Cette séparation favorise à la fois la crédibilité des résultats et le passage à l'échelle. Nous avons choisi de comparer notre proposition avec un modèle microscopique standard à travers plusieurs scénarios de simulation. Il ressort de notre comparaison que notre modèle permet de reproduire des résultats plus crédibles du point de vue d'un observateur extérieur et plus proches des études empiriques connues sur les déplacements des piétons.

The demand for automated decision making, learning and inference in uncertain, risk sensitive and dynamically changing situations presents a challenge: to design computational approaches that promise to be widely deployable and flexible to adapt on the one hand, while offering reliable guarantees on safety on the other. The tension between these desiderata has created a gap that, in spite of intensive research and contributions made from a wide range of communities, remains to be filled. This represents an intriguing challenge that provided motivation for much of the work presented in this thesis. With these desiderata in mind, this thesis makes a number of contributions towards the development of algorithms for automated decision-making and inference under uncertainty. To facilitate inference over unobserved effects of actions, we develop machine learning approaches that are suitable for the construction of models over dynamical laws that provide uncertainty bounds around their predictions. As an example application for conservative decision-making, we apply our learning and inference methods to control in uncertain dynamical systems. Owing to the uncertainty bounds, we can derive performance guarantees of the resulting learning-based controllers. Furthermore, our simulations demonstrate that the resulting decision-making algorithms are effective in learning and controlling under uncertain dynamics and can outperform alternative methods. Another set of contributions is made in multi-agent decision-making which we cast in the general framework of optimisation with interaction constraints. The constraints necessitate coordination, for which we develop several methods. As a particularly challenging application domain, our exposition focusses on collision avoidance. Here we consider coordination both in discrete-time and continuous-time dynamical systems. In the continuous-time case, inference is required to ensure that decisions are made that avoid collisions with adjustably high certainty even when computation is inevitably finite. In both discrete-time and finite-time settings, we introduce conservative decision-making. That is, even with finite computation, a coordination outcome is guaranteed to satisfy collision-avoidance constraints with adjustably high confidence relative to the current uncertain model. Our methods are illustrated in simulations in the context of collision avoidance in graphs, multi-commodity flow problems, distributed stochastic model-predictive control, as well as in collision-prediction and avoidance in stochastic differential systems. Finally, we provide an example of how to combine some of our different methods into a multi-agent predictive controller that coordinates learning agents with uncertain beliefs over their dynamics. Utilising the guarantees established for our learning algorithms, the resulting mechanism can provide collision avoidance guarantees relative to the a posteriori epistemic beliefs over the agents' dynamics.

Esquemas de Rejeição de Cargas (RC) por subfrequência, utilizados para manter a frequência de operação de um Sistema Elétrico de Potência (SEP) próxima ao seu valor nominal, precisam ser criteriosamente projetados a fim de diminuir os riscos de colapso generalizado do sistema. Entretanto, pelos métodos convencionais, a quantidade de carga a ser rejeitada não leva em consideração a dinamicidade intrínseca do sistema, sendo baseada em conjecturas estáticas sobre porções do SEP. Como resultado, a redução da carga geralmente não é eficiente, gerando rejeição insuficiente ou excessiva. Neste cenário, este trabalho propõe um novo esquema para o controle da frequência em comparação aos processos de RC usualmente empregados. Com o propósito de superar as limitações e melhorar as principais funções desses processos é proposto um Sistema Multi Agentes (SMA) centralizado que irá coordenar as diversas etapas de monitoramento, processamento e tomada de decisão nos barramentos disponíveis para corte em situações de subfrequência. Busca-se dessa forma, desconectar o menor montante de cargas do sistema, por um curto espaço de tempo e com menor perturbação da frequência. Neste sentido, uma malha de controle fechada foi desenvolvida a partir da simulação de um sistema elétrico de potência completo via o Real Time Digital Simulator (RTDS). O SMA foi embarcado em um sistema integrado de hardware e software em tempo real para teste e validação da metodologia proposta. No contexto delineado, uma métrica de avaliação foi proposta para comparar o método proposto com outras duas filosofias convencionais de RC. Os resultados obtidos permitem evidenciar o bom desempenho do SMA frente às duas filosofias convencionais, principalmente no que se refere ao montante de carga a ser rejeitado, com boa aproximação do valor esperado. / Automatic Under Frequency Load Shedding (AUFLS) schemes, used to maintain the frequency of an electric power system close to the nominal value, need to be carefully designed in order to reduce the risk of a widespread system collapse. However, the conventional methods do not take into account the inherent dynamics of an electric system and they are based on static assumptions. As a result, the shedding is generally not efficient, causing insufficient or excessive load discontinuity. In this scenario, this work proposes a new scheme for controlling the frequency compared to the AUFLS processes usually employed. In order to overcome the limitations of the methods usually employed and to improve the main functions of the AUFLS schemes, this work proposes a centralized MultiAgent System (MAS) that will coordinate the various stages of the monitoring and decision making process. The MAS seeks to disconnect a minimum amount of loads, in a short period of time and with less disturbance of the system frequency. A Hardware in Loop (HIL) configuration was developed from the simulation of a full electric system using the Real Time Digital Simulator (RTDS). The MAS was embedded in a real time system, consisting of hardware and software to test and validate the proposed methodology. In addition, a scoring metric evaluation is defined in order to compare other two conventional AUFLS philosophies. The results show good performance of the proposed MAS. The shedding was carried out in a single step and the amount of load shed was very close to the expected value.

Made available in DSpace on 2016-03-22T17:26:22Z (GMT). No. of bitstreams: 1 dm2_Luciano_vargas.pdf: 637463 bytes, checksum: b08b63e8c6a347cd2c86fc24fdfd8986 (MD5) Previous issue date: 2009-03-31 / The study and development of systems to control interactions in multiagent systems is an open problem in Artificial Intelligence. The system of social exchange values of Piaget is a social approach that allows for the foundations of the modeling of interactions between agents, where the interactions are seen as service exchanges between pairs of agents, with the evaluation of the realized or received services, thats is, the investments and profits in the exchange, and credits and debits to be charged or received, respectively, in future exchanges. This evaluation may be performed in different ways by the agents, considering that they may have different exchange personality traits. In an exchange process along the time, the different ways in the evaluation of profits and losses may cause disequilibrium in the exchange balances, where some agents may accumulate profits and others accumulate losses. To solve the exchange equilibrium problem, we use the Partially Observable Markov Decision Processes (POMDP) to help the agent decision of actions that can lead to the equilibrium of the social exchanges. Then, each agent has its own internal process to evaluate its current balance of the results of the exchange process between the other agents, observing its internal state, and with the observation of its partner s exchange behavior, it is able to deliberate on the best action it should perform in order to get the equilibrium of the exchanges. Considering an open multiagent system, it is necessary a mechanism to recognize the different personality traits, to build the POMDPs to manage the exchanges between the pairs of agents. This recognizing task is done by Hidden Markov Models (HMM), which, from models of known personality traits, can approximate the personality traits of the new partners, just by analyzing observations done on the agent behaviors in exchanges. The aim of this work is to develop an hybrid agent architecture for the self-regulation of social exchanges between personalitybased agents in a open multiagent system, based in the BDI (Beliefs, Desires, Intentions) architecture, where the agent plans are obtained from optimal policies of POMDPs, which model personality traits that are recognized by HMMs. To evaluate the proposed approach some simulations were done considering (known or new) different personality traits / O estudo e desenvolvimento de sistemas para o controle de interações em sistemas multiagentes é um tema em aberto dentro da Inteligência Artificial. O sistema de valores de trocas sociais de Piaget é uma abordagem social que possibilita fundamentar a modelagem de interações de agentes, onde as interações são vistas como trocas de serviços entre pares de agentes, com a valorização dos serviços realizados e recebidos, ou seja, investimentos e ganhos na troca realizada, e, também os créditos e débitos a serem cobrados ou recebidos, respectivamente, em trocas futuras. Esta avaliação pode ser realizada de maneira diferenciada pelos agentes envolvidos, considerando que estes apresentam traços de personalidade distintos. No decorrer de processo de trocas sociais a forma diferenciada de avaliar os ganhos e perdas nas interações pode causar desequilíbrio nos balanços de trocas dos agentes, onde alguns agentes acumulam ganhos e outros acumulam perdas. Para resolver a questão do equilíbrio das trocas, encontrou-se nos Processos de Decisão de Markov Parcialmente Observáveis (POMDP) uma metodologia capaz de auxiliar a tomada de decisões de cursos de ações na busca do equilíbrio interno dos agentes. Assim, cada agente conta com um mecanismo próprio para avaliar o seu estado interno, e, de posse das observações sobre o comportamento de troca dos parceiros, torna-se apto para deliberar sobre as melhores ações a seguir na busca do equilíbrio interno para o par de agentes. Com objetivo de operar em sistema multiagentes aberto, torna-se necessário um mecanismo para reconhecer os diferentes traços de personalidade, viabilizando o uso de POMDPs nestes ambientes. Esta tarefa de reconhecimento é desempenhada pelos Modelos de Estados Ocultos de Markov (HMM), que, a partir de modelos de traços de personalidade conhecidos, podem inferir os traços aproximados de novos parceiros de interações, através das observações sobre seus comportamentos nas trocas. O objetivo deste trabalho é desenvolver uma arquitetura de agentes híbrida para a auto-regulação de trocas sociais entre agentes baseados em traços de personalidade em sistemas multiagentes abertos. A arquitetura proposta é baseada na arquitetura BDI (Beliefs, Desires, Intentions), onde os planos dos agentes são obtidos através de políticas ótimas de POMDPs, que modelam traços de personalidade reconhecidos através de HMMs. Para avaliar a proposta, foram realizadas simulações envolvendo traços de personalidade conhecidos e novos traços

Esquemas de Rejeição de Cargas (RC) por subfrequência, utilizados para manter a frequência de operação de um Sistema Elétrico de Potência (SEP) próxima ao seu valor nominal, precisam ser criteriosamente projetados a fim de diminuir os riscos de colapso generalizado do sistema. Entretanto, pelos métodos convencionais, a quantidade de carga a ser rejeitada não leva em consideração a dinamicidade intrínseca do sistema, sendo baseada em conjecturas estáticas sobre porções do SEP. Como resultado, a redução da carga geralmente não é eficiente, gerando rejeição insuficiente ou excessiva. Neste cenário, este trabalho propõe um novo esquema para o controle da frequência em comparação aos processos de RC usualmente empregados. Com o propósito de superar as limitações e melhorar as principais funções desses processos é proposto um Sistema Multi Agentes (SMA) centralizado que irá coordenar as diversas etapas de monitoramento, processamento e tomada de decisão nos barramentos disponíveis para corte em situações de subfrequência. Busca-se dessa forma, desconectar o menor montante de cargas do sistema, por um curto espaço de tempo e com menor perturbação da frequência. Neste sentido, uma malha de controle fechada foi desenvolvida a partir da simulação de um sistema elétrico de potência completo via o Real Time Digital Simulator (RTDS). O SMA foi embarcado em um sistema integrado de hardware e software em tempo real para teste e validação da metodologia proposta. No contexto delineado, uma métrica de avaliação foi proposta para comparar o método proposto com outras duas filosofias convencionais de RC. Os resultados obtidos permitem evidenciar o bom desempenho do SMA frente às duas filosofias convencionais, principalmente no que se refere ao montante de carga a ser rejeitado, com boa aproximação do valor esperado. / Automatic Under Frequency Load Shedding (AUFLS) schemes, used to maintain the frequency of an electric power system close to the nominal value, need to be carefully designed in order to reduce the risk of a widespread system collapse. However, the conventional methods do not take into account the inherent dynamics of an electric system and they are based on static assumptions. As a result, the shedding is generally not efficient, causing insufficient or excessive load discontinuity. In this scenario, this work proposes a new scheme for controlling the frequency compared to the AUFLS processes usually employed. In order to overcome the limitations of the methods usually employed and to improve the main functions of the AUFLS schemes, this work proposes a centralized MultiAgent System (MAS) that will coordinate the various stages of the monitoring and decision making process. The MAS seeks to disconnect a minimum amount of loads, in a short period of time and with less disturbance of the system frequency. A Hardware in Loop (HIL) configuration was developed from the simulation of a full electric system using the Real Time Digital Simulator (RTDS). The MAS was embedded in a real time system, consisting of hardware and software to test and validate the proposed methodology. In addition, a scoring metric evaluation is defined in order to compare other two conventional AUFLS philosophies. The results show good performance of the proposed MAS. The shedding was carried out in a single step and the amount of load shed was very close to the expected value.

The main aim of transport stakeholders has always been to transport freight efficiently, as this efficiency contributes to the growth and success of their business. A country like Namibia is no different as the efficiency of transport lies in the effective utilisation of carrier capacity in any direction. Due to the various types of freight, transport operators rarely have the capacity to cover all freight movement requests. This research put the empty runs experienced by most of the Namibian transporters at 33%. Empty runs could however be reduced through collaboration and sharing of capacity among transport stakeholders. Multi-agent systems (MAS) are various individual computer agents that are configured independently to interact with other agents to achieve one goal. These systems have been explored as an approach to achieve collaboration among transporter stakeholders. Taking into consideration the characteristics and requirements of MAS, this research was able to conduct a feasibility of its implementation within Namibia. Concluding with an evaluation of available Multi-agent based systems that could achieve collaboration and reduce empty runs in the Namibian transport environment. / Information Science / M. Tech. (Information Technology)

Le CERV constitue à Brest un pôle d'excellence en réalité virtuelle à vocation européenne. Les systèmes que l'on cherche à modéliser sont de plus en plus complexes, du fait essentiellement de la diversité des composants, de la diversité des structures et de la diversité des interactions mises en jeu. Un système est alors a priori un milieu ouvert (apparition/disparition dynamique de composants) et hétérogène (morphologies et comportements variés). La réalité virtuelle implique pleinement l'utilisateur humain dans la simulation, rejoignant ainsi l'approche de la conception. La majorité des travaux en réalité virtuelle concerne l'immersion sensorimotrice de l'utilisateur humain au sein d'univers. Ces univers virtuels offrent à l'utilisateur la sensation d'être dans l'environnement et lui donne la possibilité d'y agir. Pour être complet, il faut également "qu'il s'y passe quelque chose", et pas seulement en terme de résultat des actions de l'utilisateur. Les entités qui peuplent les univers virtuels doivent donc avoir un comportement autonome. Ceci soulève la question suivante : comment doter une entité d'un comportement autonome dans un environnement virtuel complexe auquel l'homme participe ? Des techniques d'intelligence artificielle symbolique ont déjà été appliquées pour définir ces comportements. Mais ces techniques montrent très vite leurs limites car elles sont principalement basées sur des règles de comportements mises a priori par le concepteur. Or, dans des mondes virtuels complexes (simulation ouverte, hétérogène et participative), plusieurs entités vont avoir des comportements imprédictibles (variabilité comportementale des entités autonomes, libre arbitre de l'utilisateur), créant ainsi des situations toujours nouvelles. Et face à une situation non prévue par le programmeur, les entités auront le plus souvent des comportements inadaptés. C'est pourquoi les méthodologies tirées des systèmes artificiels adaptatifs peuvent contribuer à pallier ces limitations. Le travail que je poursuis porte sur la thématique de l'adaptation de comportements d'entités autonomes en environnement virtuel participatif. Adapter son comportement, c'est effectuer des transformations conduisant à s'adapter à son environnement. Cette adaptation aura pour objectif de rendre le comportement de l'entité virtuelle le plus crédible possible (ressemblant à un comportement humain). Pour cela, nous prenons le parti de considérer que l'entité doit apprendre au fur et à mesure des expériences, elle doit anticiper le comportement des autres entités et les conséquences sur l'environnement, elle doit également exploiter la présence de l'utilisateur humain dans l'univers virtuel pour adapter son comportement. Imaginons un monde virtuel où chaque entité, au même titre qu'un humain, aurait son propre comportement qui évolue automatiquement pendant la simulation. C'est tout l'enjeu des travaux de recherche présentés ici.

Le concept de réseau offre un modèle de représentation pour une grande variété d'objets et de systèmes, aussi bien naturels que sociaux, dans lesquels un ensemble d'entités homogènes ou hétérogènes interagissent entre elles. Il est aujourd'hui employé couramment pour désigner divers types de structures relationnelles. Pourtant, si chacun a une idée plus ou moins précise de ce qu'est un réseau, nous ignorons encore souvent les implications qu'ont ces structures dans de nombreux phénomènes du monde qui nous entoure. C'est par exemple le cas de processus tels que la diffusion d'une rumeur, la transmission d'une maladie, ou même l'émergence de sujets d'intérêt commun à un groupe d'individus, dans lesquels les relations que maintiennent les individus entre eux et leur nature s'avèrent souvent être les principaux facteurs déterminants l'évolution du phénomène. C'est ainsi que l'étude des réseaux est devenue l'un des domaines émergents du 21e siècle appelé la "Science des réseaux". Dans ce mémoire, nous abordons trois problèmes de la science des réseaux: le problème de la diffusion dans les réseaux sociaux, où nous nous sommes intéressés plus particulièrement à l'impact de la dynamique du réseau sur le processus de diffusion, le problème de l'analyse des réseaux sociaux, dans lequel nous avons proposé une solution pour tirer parti de l'ensemble des informations disponibles en combinant les informations sur la structure du réseau et les attributs des noeuds et le problème central de la collecte de données sociales, où nous nous sommes intéressés au cas particulier de la collecte de données en milieux sauvages.