Récemment, plusieurs applications, dans lesquelles différentes entités interagissent dans un environnement dynamique, soulignent l’intérêt de l’utilisation des architectures multi-agents. Ces architectures offrent, dans ce cadre, un certain nombre d’avantages, tels que l’autonomie, la réactivité et la capacité de prise de décision. Elles manquent cependant de capacité sociale et de connaissances sur son environnement, notamment lorsqu’il s’agit d’un environnement dynamique. En effet, quand un agent interagit avec le monde réel, il doit prendre en compte les évènements qui peuvent survenir tout en considérant centaines contraintes telles que le temps et l’espace. En outre, les agents doivent faire face à l’incertitude liée aux applications réelles afin de fournir une représentation fidèle du monde réel. Dans le cadre de cette thèse, nous proposons un modèle formel de recommandation des plans qui améliore le processus de prise de décision des agents dans un environnement spatio-temporel et incertain. Pour formaliser les comportements cognitifs des agents dans notre système nommé CARS, en anglais ``Cognitive Agent-based Recommender System '', nous avons étendu l’architecture BDI qui se base sur le modèle `` Croyance-Désir-Intention'' pour prendre en compte les différents contextes liés à des applications réelles en particulier le contexte social. Par ailleurs, nous avons également utilisé la théorie possibiliste afin de considérer l’incertitude dans l’état motivationnel d’un agent (c’est à dire ses croyances, désirs, objectifs ou intentions). Pour répondre aux besoins des applications réelles, tels que les systèmes de recommandation relatives au trafic et navigation, nous proposons une représentation spatiotemporelle des croyances et des intentions d’un agent. Cette représentation permettra l’anticipation de certaines intentions, de manière à recommander un plan qui sera optimal pour un utilisateur. Compte tenu l’incomplétude/l’imprécision liée aux données spatiotemporelles, nous avons étendu le modèle proposé pour raisonner avec des croyances et intentions floues. Une évaluation du modèle proposé a été menée en utilisant une simulation multi-agent, dans un scenario réel de circulation routière. Cette simulation a offert un environnement virtuel qui a mis en lumière, après avoir testé les différentes fonctionnalités du modèle, les principaux points forts ainsi que leslacunes liées à l’architecture multi-agents proposée. / Recently, many real-world applications where different entities interact in a dynamic environment, consider the use of agents in their architectures due principally to their autonomy, reactivity and decision-making abilities. Though these systems can be made intelligent, using Artificial Intelligence (AI) techniques, agents still lack of social abilities and have limited knowledge of their environment and in particular when it comes to a dynamic environment. In fact, when operating in the real world, agents need to deal with unexpected events considering both changes in time and space. Moreover, agents must face the uncertainty, which pervades real-world scenarios in order to provide an accurate representation of the world. In this thesis, we introduce and evaluate a formal framework for recommending plans to agents in the decision making process, when they deal with uncertain spatio-temporal information. The agent-based architecture we propose to address this issue, called CARS (Cognitive Agent-based Recommender System), has been designed by extending the well-known Belief-Desire-Intention (BDI) architecture to incorporate further capabilities to support reasoning with different types of contextual information, including the social context. Uncertainty on the agent's beliefs, desires and intentions is modeled using possibility theory. To meet the requirements of real-world applications, e.g., traffic and navigation recommendation systems, we define a spatio-temporal representation of the agents' beliefs and intentions. Using such a formal framework, anticipatory reasoning about intentional dynamics can be performed with the aim to recommend an optimal plan to a certain user. Since spatio-temporal data is often considered as incomplete and/or vague, we extended the formal framework with a fuzzy representation of spatio-temporal beliefs and intentions. The framework is evaluated through an Agent Based Simulation (ABS) in a real-world traffic scenario. This ABS allowed us to create a virtual environment to test the impact of the different features of our framework as well as to evaluating the main strengths and weaknesses of the proposed agent architecture.

[pt] A área da saúde desponta como um terreno fértil para a pesquisa científica em Tecnologia da Informação. As atividades de pesquisa na área permitem abordar diversas questões para promover o desenvolvimento tecnológico. Além disso, em relação aos aplicativos para dispositivos móveis, as principais lojas de aplicativos registraram um crescimento significativo no número de aplicativos de saúde disponíveis. Esses crescimentos encontram uma de suas causas na realidade pandêmica que enfrentamos desde o início de 2020. Para lidar com os diferentes desafios de saúde apresentados atualmente, o uso de sistemas multiagentes tem sido considerado uma boa abordagem para lidar, por exemplo, com distribuição, pró-atividade e autonomia dos sistemas. Embora várias plataformas conhecidas utilizem agentes de software, algumas delas não oferecem suporte adequado para o desenvolvimento de agentes, como a plataforma iOS. Assim, nesta dissertação propusemos um framework BDI para iOS que visa apoiar o desenvolvimento de aplicativos móveis de saúde com agentes de software. Esse framework é chamado de Swift Agent Development framework for health (SADE4Health) e usa recursos nativos do iOS, como o Healthkit, que é um repositório central de dados de saúde e condicionamento físico para acessar e compartilhar dados, mantendo a privacidade e o controle do usuário. Para mostrar como o framework suporta o desenvolvimento de novos aplicativos iOS com agentes de software, são explicados os passos mínimos necessários para criar um agente usando os recursos de saúde oferecidos pelo iOS, assim como um cenário de uso modelado a partir deles. Além disso, é apresentado um cenário de uso relacionado ao monitoramento remoto de sinais vitais de pacientes, ilustrando como desenvolver uma instância do framework proposto. Por fim, uma avaliação empírica com desenvolvedores iOS para medir a usabilidade do framework trouxe importantes achados. / [en] The healthcare area is emerging as a fertile ground for scientific research in Information Technology. Research activities in this field allow us to address several issues to promote technological development. In addition, regarding mobile device apps, the leading app stores registered significant growth in the number of available eHealth apps. One of the causes of this growth derives from the pandemic reality we have faced since the beginning of 2020. In order to confront the different health challenges presented currently, the use of multi-agent systems has been considered a good approach, dealing with, for example, distribution, pro-activity and autonomy of systems. Although several known platforms that use software agents, some of them do not offer appropriate support to develop agents, such as the iOS platform. Thus, in this dissertation we have proposed a BDI framework for iOS that aims to support the development of health mobile apps with software agents. This is known as Swift Agent Development Framework for Health (SADE4Health) and it uses native iOS resources, such as Healthkit, comprising a central repository for health and fitness data to access and share data while maintaining the user s privacy and control. To show how the framework supports the development of new iOS apps with software agents, the minimum necessary steps to create an agent using health features offered by iOS are explained, as well as a modeled use scenario based on it. Furthermore, a use scenario related to remote monitoring of patients vital signs that illustrates how to develop an instance of the proposed framework is presented. Finally, an empirical evaluation with iOS developers to measure the framework usability brought important findings.

本論文所定義之「網路群體犯罪」，不同於組織犯罪般有結構的犯罪團體，亦非為了追求共同利益而合作的共犯夥伴，而是網路使用者自發性互動行為下逐漸浮現的群體近似犯罪行為，並且普遍存在於當今各式各樣的網際網路社群，以各種不同的樣貌與形式展現。本研究以Sutherland（1978）提出之差別接觸理論與Bandura（1977）提出之社會學習理論為基礎，運用理論相關的元素與概念作為食材與食譜，以BDI代理人模式為方法來設計網路群體犯罪之模擬模式，透過動態模擬群體犯罪在不同條件下展現不同之面貌。更運用Watts（2003）主張的網路科學概念與分析方法，來分析犯罪關係網絡之特性，本研究藉由控制網路社群之使用者人數（Size）與初始犯罪率（ICR）來觀察不同組合之下所演化的網路結構差異，並從四個衡量指標：犯罪技能平均數、群聚係數、前10%使用者平均連結度、連結度小於10之比率，標示演化之網路結構的特徵。研究結果發現：1. 犯罪技能擴散的速度受到ICR高低的影響，當ICR越高的時候犯罪技能擴散的速度越快，反之，當ICR較低的時候犯罪技能擴散速度隨之減緩。2. 當ICR超越某一特定臨界值之後，使用者擁有的犯罪技能平均數與所屬社群人數成正向關係。3. ICR的高低對於群聚係數的高低有反向關係，當ICR越高則群聚係數越低，反之，當ICR越低時群聚係數越高。4. 社群使用者人數越多的情況下，群聚係數越低。5. 前10%使用者的平均連結度有隨著演化次數逐漸增加的趨勢。6. 初始犯罪率的高低與前10% 使用者的平均連結度成反比關係。7. 不論演化次數、社群人數多寡與初始犯罪率值之高低，均僅有少數犯罪者擁有高度的連結，絕大多數的使用者或犯罪者其連結度數均不高（符合power law分佈）。 / Collective crime is an emerging phenomenon along with collective intelligence in recent years. It is defined as a form of universally distributed crime originated from spontaneous interaction among community users in this paper. The issues that collective crime addresses focus on deviant or criminal behavior existing in common groups or crowds rather than traditional topics at computer crime or cybercrime. The theories, “differential association” proposed by criminologist Sutherland(1978) and “social learning” proposed by sociologist Bandura(1977), underpin the explanation of collective crime phenomena and the model design of agent-based simulation. The detection function of collective crime consists of the evolving network function based on the micro-simulation and an analysis of the function along with four indicators: average amount of crime skills, average cluster coefficient, average degree of top 10% users, and rate of users with degrees smaller than 10. The research findings are: 1. A community with higher initial crime rate (ICR) results in faster spreading of crime skills. 2. A negative relationship between the community size and the average amounts of crime skills exists, as ICR exceeds a threshold. 3. As ICR gets increasing, the average cluster coefficient gets decreasing, and vice versa. 4. The average cluster coefficient gets decreasing along with increasing community size. 5. The average degree of top 10% users gets increasing along time. 6. A negative relationship exists between ICR and the average degree of the top 10% users. 7. The distribution of the degrees of community users follows the scale-free power law distribution – whatever the network evolution times, community size and ICR are, most of the community users have fewer degrees and only few criminals have pretty high degrees relatively.

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