Recent events have probably lead us to wonder why people make decisions that seem to be irrational, and that go against any easily understandable logic. The fact that these decisions are emotionally driven often explains what, at first glance, does not have a plausible explanation. Evidence has been found that proves that emotions and other affective characteristics guide decisions beyond a purely rational deliberation. Understanding the way emotions take place, the way emotions change, and/or the way emotions influence behavior, has traditionally been a concern of several fields including psychology and neurology. Moreover, other sciences such as behavioral economics, artificial intelligence, and in general, all sciences that aim to understand, explain, or simulate human behavior, acknowledge the important role of affective characteristics in this task. Specifically, artificial intelligence uses psychological findings in order to create agents that simulate human behavior. Nevertheless, individual research efforts in modeling affective characteristics are often overlapped, short of integration, and they lack of a common conceptual system. This deprives individual researches of the exchange and cooperation's inherent benefits, and makes the task of computationally simulating affective characteristics more difficult. Although much individual effort has been put in classifying, formalizing and modeling emotions and emotion theories on some fields, recognized researchers of emotions' and affective processes' modeling report that a common formal language, an informal conceptual system, and a general purpose affective agent architecture will greatly improve the interdisciplinary exchange and the intradisciplinary coordination. The research literature proposes a wide amount of affective models that deal with some of: relationship between emotions and cognition, relationship between emotions and behavior, emotions and their evolutionary account, emotions for appraising situations, emotion regulation, etc. These models are useful tools for addressing particular emotion-related issues. Furthermore, computational approaches that are based on particular psychological theories have also been proposed. They often address domain specific issues starting from a specific psychological theory. In such solutions, the absence of a common conceptual system and/or platform, makes difficult the feedback between psychological theories and computational approaches. This thesis systematizes and formalizes affect-related theories, what can benefit the interdisciplinary exchange, the intradisciplinary coordination, and hence, allows the improvement of involved disciplines. Specifically this thesis makes the following contributions: (1) a theoretical framework that includes the main processes and concepts that a model of an affective agent with practical reasoning should have; (2) a general-purpose affective agent architecture that shares the concepts of the proposed theoretical framework; (3) an implementation-independent formal language for designing affective agents that have the proposed architecture; and (4) a specific agent language for implementing affective agents which is an extension of a BDI language. Some studies with human participants have helped to validate the contributions of this thesis. They include classical games of game theory, and an study with 300 participants, which have provided the necessary information to evaluate the contributions. The validation has been performed in three directions: determine whether the proposed computational approach represents better the human behavior than traditional computational approaches; determine whether this approach allows to improve psychological theories used by default; and determine whether the proposed affective agents' behavior is closer to human behavior than the behavior of a purely rational agent. / Probablemente algunos eventos recientes nos han conducido a preguntarnos por qué las personas toman decisiones aparentemente irracionales y en contra de alguna lógica fácilmente comprensible. El hecho de que estas decisiones estén bajo la influencia de las emociones a menudo explica lo que, a primera vista, parece no tener una explicación aceptable. En este sentido, se han encontrado evidencias que prueban que las emociones y otras características afectivas condicionan las decisiones más allá de una deliberación meramente racional. Entender cómo las emociones tienen lugar, cómo cambian y cómo influyen en el comportamiento, ha sido tradicionalmente de interés para muchos campos de investigación, incluyendo la psicología y la neurología. Además, otras ciencias como la economía conductual o la inteligencia artificial reconocen el importante papel de las características afectivas en esta tarea. Específicamente, la inteligencia artificial utiliza los resultados obtenidos en psicología para crear agentes que simulan el comportamiento humano. Sin embargo, a menudo los esfuerzos individuales de investigación en el modelado del afecto se solapan, carecen de la suficiente integración y de un sistema conceptual común. Esto limita a las investigaciones individuales para disponer de los beneficios que ofrecen el intercambio y la cooperación, y hace más compleja la tarea de simular los procesos afectivos. Las emociones y teorías relacionadas han sido clasificadas, formalizadas y modeladas. No obstante, reconocidos investigadores argumentan que un lenguaje formal común, un sistema conceptual informal y una arquitectura de agentes de propósito general, mejorarán significativamente el intercambio interdisciplinar y la coordinación intradisciplinar. En la literatura se propone una amplia cantidad de modelos afectivos que modelan: la relación entre las emociones y la cognición, la relación entre las emociones y el comportamiento, las emociones para evaluar las situaciones, la regulación de emociones, etc. Estos modelos son herramientas útiles para abordar aspectos particulares relacionados con las emociones. Además, se han realizado propuestas computacionales que abordan aspectos específicos sobre la base de teorías psicológicas específicas. En éstas soluciones, la ausencia de una plataforma y/o sistema conceptual dificulta la retroalimentación entre las teorías psicológicas y las propuestas computacionales. Esta tesis sistematiza y formaliza teorías relacionadas con el afecto, lo cual beneficia el intercambio interdisciplinar y la coordinación intradisciplinar, y por tanto, permite el desarrollo de las disciplinas correspondientes. Específicamente esta tesis realiza las siguientes contribuciones: (1) una plataforma teórica que incluye los conceptos y procesos principales que debería poseer un modelo de agentes afectivos con razonamiento práctico; (2) una arquitectura de agentes de propósito general que comparte los conceptos de la plataforma teórica propuesta; (3) un lenguaje formal independiente de la implementación, para diseñar agentes afectivos que poseen la arquitectura propuesta; y (4) un lenguaje de agentes específico para implementar agentes afectivos el cual es un extensión de un lenguaje BDI. Algunos estudios con participantes humanos han ayudado a validar las contribuciones de esta tesis. Estos incluyen juegos clásicos de teoría de juegos y un estudio con 300 participantes, los cuales han proporcionado la información necesaria para evaluar las contribuciones. La validación se ha realizado en tres direcciones: determinar si la propuesta computacional que se ha realizado representa mejor el comportamiento humano que propuestas computacionales tradicionales; determinar si esta propuesta permite mejorar las teorías psicológicas empleadas por defecto; y determinar si el comportamiento de los agentes afectivos propuestos se acerca más al comportamiento humano que el compor / Probablement alguns esdeveniments recents ens han conduït a preguntar-nos per què les persones prenen decisions que aparentment són irracionals i que van en contra d'algun tipus de lògica fàcilment comprensible. El fet que aquestes decisions estiguin sota la influència de les emocions sovint explica el que, a primera vista, sembla no tenir una explicació acceptable. En aquest sentit, s'han trobat evidències que proven que les emocions i altres característiques afectives condicionen les decisions més enllà d'una deliberació merament racional. Entendre com les emocions tenen lloc, com canvien i com influeixen en el comportament, ha estat tradicionalment d'interès per a molts camps d'investigació, incloent la psicologia i la neurologia. A més, altres ciències com l'economia conductual, la intel·ligència artificial i, en general, totes les ciències que intenten entendre, explicar o simular el comportament humà, reconeixen l'important paper de les característiques afectives en aquesta tasca. Específicament, la intel·ligència artificial utilitza els resultats obtinguts en psicologia per crear agents que simulen el comportament humà. No obstant això, sovint els esforços individuals d'investigació en el modelatge de l'afecte es solapen, no tenen la suficient integració ni compten amb un sistema conceptual comú. Això limita a les investigacions individuals, que no poden disposar dels beneficis que ofereixen l'intercanvi i la cooperació, i fa més complexa la tasca de simular els processos afectius. Les emocions i teories relacionades han estat classificades, formalitzades i modelades. No obstant això reconeguts investigadors argumenten que un llenguatge formal comú, un sistema conceptual informal i una arquitectura d'agents de propòsit general, milloraran significativament l'intercanvi interdisciplinar i la coordinació intradisciplinar. En la literatura es proposa una àmplia quantitat de models afectius que modelen: la relació entre les emocions i la cognició, la relació entre les emocions i el comportament, les emocions per avaluar les situacions, la regulació d'emocions, etc. Aquests models són eines útils per abordar aspectes particulars relacionats amb les emocions. A més, s'han realitzat propostes computacionals que aborden aspectes específics sobre la base de teories psicològiques específiques. En aquestes solucions, l'absència d'una plataforma i/o sistema conceptual dificulta la retroalimentació entre les teories psicològiques i les propostes computacionals. Aquesta tesi sistematitza i formalitza teories relacionades amb l'afecte, la qual cosa beneficia l'intercanvi interdisciplinar i la coordinació intradisciplinar, i per tant, permet el desenvolupament de les disciplines corresponents. Específicament aquesta tesi realitza les següents contribucions: (1) una plataforma teòrica que inclou els conceptes i processos principals que hauria de posseir un model d'agents afectius amb raonament pràctic; (2) una arquitectura d'agents de propòsit general que comparteix els conceptes de la plataforma teòrica proposta; (3) un llenguatge formal independent de la implementació, per dissenyar agents afectius que posseeixen l'arquitectura proposada; i (4) un llenguatge d'agents específic per implementar agents afectius el qual és un extensió d'un llenguatge BDI. Alguns estudis amb participants humans han ajudat a validar les contribucions d'aquesta tesi. Aquests inclouen jocs clàssics de teoria de jocs i un estudi amb 300 participants, els quals han proporcionat la informació necessària per avaluar les contribucions. La validació s'ha realitzat en tres direccions: determinar si la proposta computacional que s'ha realitzat representa millor el comportament humà que propostes computacionals tradicionals; determinar si aquesta proposta permet millorar les teories psicològiques emprades per defecte; i determinar si el comportament dels agents afectius proposats s'acosta més al / Alfonso Espinosa, B. (2017). Agents with Affective Traits for Decision-Making in Complex Environments [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90497

In nature, many social species demonstrate collective behavior ranging from coordinated motion in flocks of birds and schools of fish to collective decision making in humans. Such distinct behavioral patterns at the group level are the consequence of local interactions among the individuals. We can learn from these biological systems, which have successfully evolved to operate in noisy and fault-prone environments, and understand how these complex interactions can be applied to engineered systems where robustness remains a major challenge. This dissertation addresses a two-scale approach to study these interactions- one in larger scale, where we are interested in the information exchange in a group and how it enables the group to reach a common decision, and the other in a smaller scale, where we are focused in the presence and directionality in the information exchange in a pair of individuals. To understand the interactions at large scale, we use a graph theoretic approach to study consensus or synchronization protocols over two types of biologically-inspired interaction networks. The first network captures both collaborative and antagonistic interactions and the second considers the impact of dynamic leaders in presence of purely collaborative interactions. To study the interactions at small scale, we use an information theoretic approach to understand the directionality of information transfer in a pair of individual using a real-world data-set of animal group motion. Finally, we choose the issue of same-sex marriage in the United States to demonstrate that collective opinion formation is not only a result of negotiations among the individuals, but also reflects inherent spatial and political similarities and temporal delays. / Ph. D. / Social animals exhibit coordination often referred to as ‘collective behavior’ that results from interactions among individuals in the group. This dissertation has demonstrated how interactions can be studied using mathematical modeling, at the same time reveals that real-world interactions are even more complex. Mathematical modeling provides capabilities to introduce biologically inspired phenomena, for example, the implementation of both friendly and hostile interactions that may coexist; and the presence of leader-follower interactions, which is another determinant of collective behavior. The results may find applications in real-world networks, where hostile and leader-follower interactions are prevalent, for example international relations, online social media sites, neural networks, and biologically inspired robotic interactions. We further extend our knowledge regarding interactions by choosing real world systems, the first to understand human decision making, for example in public policies; and the second in animal group motion. Public policy adoption is generally complex and depends on a variety of factors, and no exception is same-sex marriage in the United States which has been a volatile subject for decades until nationwide legalization on June 26, 2015. We target this timely issue and explore the opinion formation of senators and state-law as they evolve over two decades to identify factors that may have affected the dynamics. We unravel geographic proximity, and state-government ideology are significant contributors to the senators opinions and the state-law adoption. Moreover, we build a state-law adoption model which captures these driving factors, and demonstrates predictive power. This study will help to understand or model other public policies that propagate via social and political change. Next we choose the system of bats to investigate navigational leadership roles as they fly in pairs from direct observation of bat swarms in flight. Pairs of bats were continuously tracked in a mountain cave in Shandong Province, China, from which three-dimensional path points are extracted and converted to one-dimensional curvature time series. The study allows us to answer the question of whether individuals fly independently of each other or interact to plan flight paths.

The security and resiliency of electric power supply to serve critical facilities are of high importance in today's world. Instead of building large electric power grids and high capacity transmission lines, an intelligent microgrid (or smart grid) can be considered as a promising power supply alternative. In recent years, multi-agent systems have been proposed to provide intelligent energy control and management systems in microgrids. Multi-agent systems offer their inherent benefits of flexibility, extensibility, autonomy, reduced maintenance and more. The implementation of a control network based on multi-agent systems that is capable of making intelligent decisions on behalf of the user has become an area of intense research. Many previous works have proposed multi-agent system architectures that deal with buying and selling of energy within a microgrid and algorithms for auction systems. The others proposed frameworks for multi-agent systems that could be further developed for real life control of microgrid systems. However, most proposed methods ignore the process of sharing energy resources among multiple distinct sets of prioritized loads. It is important to study a scenario that emphasizes on supporting critical loads during outages based on the user's preferences and limited capacity. The situation becomes further appealing when an excess DER capacity after supplying critical loads is allocated to support non-critical loads that belong to multiple users. The previous works also ignore the study of dynamic interactions between the agents and the physical systems. It is important to study the interaction and time delay when an agent issues a control signal to control a physical device in a microgrid and when the command is executed. Agents must be able to respond to the information sensed from the external environment quickly enough to manage the microgrid in a timely fashion. The ability of agents to disconnect the microgrid during emergencies should also be studied. These issues are identified as knowledge gaps that are of focus in this thesis. The objective of this research is to design, develop and implement a multi-agent system that enables real-time management of a microgrid. These include securing critical loads and supporting non-critical loads belonging to various owners with the distributed energy resource that has limited capacity during outages. The system under study consists of physical (microgrid) and cyber elements (multi-agent system). The cyber part or the multi-agent system is of primary focus of this work. The microgrid simulation has been implemented in Matlab/Simulink. It is a simplified distribution circuit that consists of one distributed energy resources (DER), loads and the main grid power supply. For the multi-agent system implementation, various open source agent building toolkits are compared to identify the most suitable agent toolkit for implementation in the proposed multi-agent system. The agent architecture is then designed by dividing overall goal of the system into several smaller tasks and assigning them to each agent. The implementation of multi-agent system was completed by identifying Roles (Role Modeling) and Responsibilities (Social and Domain Responsibilities) of agents in the system, and modeling the Knowledge (Facts), rules and ontology for the agents. Finally, both microgrid simulation and multi-agent system are connected together via TCP/IP using external java programming and a third party TCP server in the Matlab/Simulink environment. In summary, the multi-agent system is designed, developed and implemented in several simulation test cases. It is expected that this work will provide an insight into the design and development of a multi-agent system, as well as serving as a basis for practical implementation of an agent-based technology in a microgrid environment. Furthermore, the work also contributes to new design schemes to increase multi-agent system's intelligence. In particular, these include control algorithms for intelligently managing the limited supply from a DER during emergencies to secure critical loads, and at the same time supporting non-critical loads when the users need the most. / Master of Science

One of the major planning problems faced by medium and large manufacturing enterprises is the distribution of production over various (production) facilities. The need for cross-facility capacity management is most evident in the high-tech industries having capital-intensive equipment and short technology life cycle. There have been solutions proposed in the literature that are based on the lagragian decomposition method which separate the overall multiple product problem into a number of single product problems. We believe that multi-agent systems, given their distributed problem solving approach can be used to solve this problem, in its entirety, more effectively. According to other researchers who have worked in this field, auction theoretic mechanisms are a good way to solve complex production planning problems. This research study develops a multi-agent system and negotiation protocol based on combinatorial auction framework to solve the given multi-facility planning problem. The output of this research is a software library, which can be used as a multi-agent system model of the multi-product, multi-facility capacity allocation problem. The negotiation protocol for the agents is based on an iterative combinatorial auction framework which can be used for making allocation decisions in this environment in real-time. A simulator based on this library is created to validate the multi-agent model as well as the auction theoretic framework for different scenarios in the problem domain. The planning software library is created using open source standards so that it can be seamlessly integrated with scheduling library being developed as a part of the Advanced Planning and Scheduling (APS) system project or any other software suite which might require this functionality. The research contribution of this study is in terms of a new multi-agent architecture for an Advanced Planning and Control (APS) system as well as a novel iterative combinatorial auction mechanism which can be used as an agent negotiation protocol within this architecture. The theoretical concepts introduced by this research are implemented in the MultiPlanner production planning tool which can be used for generating master production plans for manufacturing enterprises. The validation process carried out on both the iterative combinatorial framework and the agent-based production planning methodology demonstrate that the proposed solution strategies can be used for integrated decision making in the multi-product, multi-facility production planning domain. Also, the software tool developed as part of this research is a robust, platform independent tool which can be used by manufacturing enterprises to make relevant production planning decisions. / Master of Science

<p dir="ltr">Increased frequency and intensity of disasters necessitate the dynamic post-disaster recovery process. Developing human mobility patterns, household return decision-making models, and agent-based simulations in disaster management has opened a new door towards more intricate and enduring recovery frameworks. Despite these opportunities, the importance of a unified framework is underestimated to identify the underlying mechanisms hindering the post-disaster recovery process. My research has been geared towards forging advancements in civil and disaster management, focusing on two main areas: (1) modeling the post-disaster recovery process and (2) identifying critical transitions within the recovery process.</p><p dir="ltr">My dissertation explores the collective and individual dynamics of post-disaster recovery across different spatial and temporal scales. I have identified the best recovery strategies for various contexts by constructing data-driven socio-physical multi-agent systems. Employing various advanced computational methodologies, including machine learning, system dynamics, causal discovery, econometrics, and network analysis, has been instrumental. I start with aggregated level analysis for post-disaster recovery. Initially, I examined the system dynamics model for the post-discovery recovery process in socio-physical systems, using normalized visit density of points of interest and power outage information. Through counterfactual analyses of budget allocation strategies, I discovered their significant impact on recovery trajectories, noting that specific budget allocations substantially enhance recovery patterns. I also revealed the urban-rural dissimilarity by the data-driven causal discovery approach. I utilized county-level normalized visit density of points of interest and nighttime light data to identify the relationship between counties. I found that urban and rural areas have similar but different recovery patterns across different types of points of interest.</p><p dir="ltr">Moving from aggregated to disaggregated level analysis on post-disaster recovery, I investigated household-level decision-making regarding disaster-induced evacuation and return behaviors. The model yielded insights into the varying influences of certain variables across urban and rural contexts. Subsequently, I developed a unified framework integrating aggregated and disaggregated level analyses through multilayer multi-agent systems to model significant shifts in the post-disaster recovery process. I evaluated various scenarios to pinpoint conditions for boosting recovery and assessing the effects of different intervention strategies on these transitions. Lastly, a comparison between mathematical models and graph convolutional networks was conducted to better understand the conditions leading to critical transitions in the recovery process. The insights and methodologies presented in this dissertation contribute to the broader understanding of the disaster recovery process in complex urban systems, advocating for a shift towards a unified framework over individual models. By harnessing big data and complex systems modeling, I can achieve a detailed quantitative analysis of the disaster recovery process, including critical transition conditions of the post-disaster recovery. This approach facilitates the evaluation of such recovery policies through inter-regional comparisons and the testing of various policy interventions in counterfactual scenarios.</p>

A cyber-physical control system (CPS) typically consists of a set of physical subsystems, their remote terminal units, a central control center (if applicable), and local communication networks that interconnect all the components to achieve a common goal. Applications include energy systems, autonomous vehicles, and collaborative robots. Ensuring stability, performance, and resilience in CPS requires thorough analysis and control design, utilizing robust algorithms to handle delays, communication failures, and potential cyber-attacks. Time delays are a challenge in CPS, particularly in teleoperation systems, where human operators remotely control robotic systems. These delays cause chattering, oscillations, and instability, making it difficult to achieve smooth and stable remote robot control. Applications like remote surgery, space exploration, and hazardous environment operations are highly susceptible to these disruptions. To address this issue, a novel passivity-shortage framework is proposed, that enables systems to maintain stability and transparency despite time-varying communication delays and environmental disturbances. CPS are prone to attacks, particularly Denial-of-Service (DoS) attacks, which disrupt the normal functioning of a network by overwhelming it with excessive internet traffic, rendering the communication channels unavailable to legitimate users. These attacks threaten the stability and functionality of CPS. To enhance resilience in multi-agent systems, novel distributed algorithms are proposed. These graph theory-based algorithms mitigate network vulnerabilities by incorporating strategically placed additional communication channels, thereby increasing tolerance to attacks in large, dynamic networks. The effectiveness of these proposed approaches is validated through simulations, experiments, and numerical examples. The passivity-shortage teleoperation strategies are tested using Phantom Omni devices and they show reduced chattering and better steady-state error convergence. A case study demonstrates how the proposed distributed algorithms effectively achieve consensus, even when some agents are disconnected from the network due to DoS attacks.

Optimizing connected automated vehicle movements through roadway intersections is a challenging problem. Traditional traffic control strategies, such as traffic signals are not optimal, especially for heavy traffic. Alternatively, centralized automated vehicle control strategies are costly and not scalable given that the ability of a central controller to track and schedule the movement of hundreds of vehicles in real-time is highly questionable. In this research, a series of fully distributed heuristic algorithms are proposed where vehicles in the vicinity of an intersection continuously cooperate with each other to develop a schedule that allows them to safely proceed through the intersection while incurring minimum delays. An algorithm is proposed for the case of an isolated intersection then a number of algorithms are proposed for a network of intersections where neighboring intersections communicate directly or indirectly to help the distributed control at each intersection makes a better estimation of traffic in the whole network. An algorithm based on the Godunov scheme outperformed optimized signalized control. The simulated experiments show significant reductions in the average delay. The base algorithm is successfully added to the INTEGRATION micro-simulation model and the results demonstrate improvements in delay, fuel consumption, and emissions when compared to roundabout, signalized, and stop sign controlled intersections. The study also shows the capability of the proposed technique to favor emergency vehicles, producing significant increases in mobility with minimum delays to the other vehicles in the network. / Ph. D. / Intelligent self-driving cars are getting much closer to reality than fiction. Technological advances make it feasible to produce such vehicles at low affordable cost. This type of vehicles is also promising to significantly reduce car accidents saving people lives and health. Moreover, the congested roads in cities and metropolitan areas especially at rush hours can benefit from this technology to avoid or at least to reduce the delays experienced by car passengers during their trips. One major challenge facing the operation of an intelligent self-driving car is how to pass an intersection as fast as possible without any collision with cars approaching from other directions of the intersection. The use of current traffic lights or stop signs is not the best choice to make the best use of the capabilities of future cars. In this dissertation, the aim is to study and propose ways to make sure the future intersections are ready for such self-driving intelligent cars. Assuming that an intersection has no type of traditional controls such as traffic lights or stop signs, this research effort shows how vehicles can pass safely with minimum waiting. The proposed techniques focus on providing lowcost solutions that do not require installation of expensive devices at intersections that makes it difficult to be approved by authorities. The proposed techniques can be applied to intersections of various sizes. The algorithms in this dissertation carefully design a way for vehicles in a network of intersections to communicate and cooperate while passing an intersection. The algorithms are extensively compared to the case of using traffic lights, stop signs, and roundabouts. Results show significant improvement in delay reduction and fuel consumption when the proposed techniques are used.

A tecnologia de agentes tem sido reconhecida como um paradigma promissor em sistemas educacionais da nova geração. Entretanto, o esforço e inflexibilidade de algumas metodologias próprias para agentesacarretam num alto custo, tempo e adaptação de escopo. Este trabalho visaavaliar alternativas de desenvolvimento de um jogo educacional médico orientado a agentes, através da aplicação de um estudo de caso, com o intuito de verificar se metodologias próprias para implementação de sistemas multiagentes trazem benefícios no resultado final da implementação do jogo, e também se os resultados alcançados na comparação de processos de desenvolvimento de cunho tradicional e ágil fazem diferença no resultado final. Desta forma, este trabalho compara três metodologias baseadas nos conceitos da Engenharia de Software através de um estudo de caso, sendo elas: O-MaSE que é uma metodologiatradicional de desenvolvimento de sistemas multiagentes e utiliza um processo de desenvolvimento tradicional; AgilePASSI que é baseada no processo de desenvolvimento ágil e específica para sistemas multiagentes; e, por último, Scrum que é uma metodologia ágil, não sendo específica para implementação de sistemas multiagentes / The agent technology has been recognized as a promising paradigm in educational systems of the new generation. However, the effort and inflexibility of some specific methodologies entail a high cost, time and adaptation scope. This work aims to validate options for developing an educational medical game oriented agents by applying an experiment in order to verify that methodologies specific to implement multi-agent systems provide benefits in the result of the implementation of the game, and also the results achieved by comparison of traditional and agile development processes makes a difference in the outcome. Thus, this paper compares three approaches based on the concepts of software engineering through an experiment, as follows: O-MaSE is a traditional methodology for the development of multi-agent systems and uses a traditional development process; AgilePASSI which is based on agile and specific development for multi-agent systems; and finally, Scrum that is an agile methodology, not specific to implementation of multi-agent systems.

A tecnologia de agentes tem sido reconhecida como um paradigma promissor em sistemas educacionais da nova geração. Entretanto, o esforço e inflexibilidade de algumas metodologias próprias para agentesacarretam num alto custo, tempo e adaptação de escopo. Este trabalho visaavaliar alternativas de desenvolvimento de um jogo educacional médico orientado a agentes, através da aplicação de um estudo de caso, com o intuito de verificar se metodologias próprias para implementação de sistemas multiagentes trazem benefícios no resultado final da implementação do jogo, e também se os resultados alcançados na comparação de processos de desenvolvimento de cunho tradicional e ágil fazem diferença no resultado final. Desta forma, este trabalho compara três metodologias baseadas nos conceitos da Engenharia de Software através de um estudo de caso, sendo elas: O-MaSE que é uma metodologiatradicional de desenvolvimento de sistemas multiagentes e utiliza um processo de desenvolvimento tradicional; AgilePASSI que é baseada no processo de desenvolvimento ágil e específica para sistemas multiagentes; e, por último, Scrum que é uma metodologia ágil, não sendo específica para implementação de sistemas multiagentes / The agent technology has been recognized as a promising paradigm in educational systems of the new generation. However, the effort and inflexibility of some specific methodologies entail a high cost, time and adaptation scope. This work aims to validate options for developing an educational medical game oriented agents by applying an experiment in order to verify that methodologies specific to implement multi-agent systems provide benefits in the result of the implementation of the game, and also the results achieved by comparison of traditional and agile development processes makes a difference in the outcome. Thus, this paper compares three approaches based on the concepts of software engineering through an experiment, as follows: O-MaSE is a traditional methodology for the development of multi-agent systems and uses a traditional development process; AgilePASSI which is based on agile and specific development for multi-agent systems; and finally, Scrum that is an agile methodology, not specific to implementation of multi-agent systems.

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.