The number of smartwatches is constantly growing and emerging in many different fields; fitness is one of the areas. To measure heart rate and other fitness-related metrics, smartwatches use Artificial Intelligence (AI) to generate real-time representations of the different metrics and provide activity notifications to encourage exercise. Feedback is essential for any learning process, and today, there is limited research on how to enable user interaction with smartwatches. This study is a design science research study focusing on how activity notifications can be designed to enable user interaction through a smartwatch with the help of persuasive design. The study resulted in seven guidelines (Minimize steps, Routine, Social deviance, Timing, Encouragement, Cognitive load, and Trigger) that were tested as a prototype through a Wizard of Oz test with smartwatch users. This study concludes the importance of not creating unhealthy behaviors and the importance of notifications that show at a suitable time, making it convenient for the user to interact. The results from this study will be valuable for developers of AI systems and smartwatches to increase a healthy population through more desirable and more tuned smartwatches.

The demand for products continues to increase in today’s society, and to meet this demandcompanies are searching for new ways to improve the performance of their workers.Therefore, there is a constant push to develop and implement new technological solutionswithin the Industry 4.0 approach. The aim of this study is to research the different pathwaysone could take when implementing these technological solutions and what challenges itwould entail, with a focus on Artificial Intelligence (AI). This is done in collaboration withSaab Surveillance within their production division, who wishes to increase theirperformance within their white-collar environment. In this study, performance is defined andmeasured through productivity. The main indicators of productivity will, therefore, be timededicated to a task as well as the potential to improve the quality of a task. The result of thisstudy is presented with a roadmap framework where seven key areas, i.e., work processes,were discovered that could benefit from AI applications. These areas were uncovered byconducting a contextual inquiry and semi-structured interviews, and were then matched withrelevant AI applications. The discovered key areas are categorized based on a cost-benefitanalysis, with the scale of; low, medium, and high. The roadmap illustrates in which areas itcould be most beneficial to implement the suggested AI applications. Using this study, Saaband other companies can make more informed decisions on the pathways for adopting newtechnological solutions that will improve the performance of their white-collar workers.

Research Background: The retail industry is undergoing a significant transformation driven by the integration of Artificial Intelligence (AI) technologies. AI implementation faces challenges, notably risk of malfunctions, which disrupt internal operations. In the context of this research, "non-consumer AI malfunctions" are defined as disruptions in AI systems that are utilized within the organizational operations of retail businesses. Our investigation is motivated by the recognition that while AI has the potential to redefine retail operations fundamentally, a nuanced understanding of how to manage and learn from AI malfunctions, particularly in non-consumer contexts, is essential for realizing this potential fully. This study underscores the importance of researching how organizations effectively respond to such malfunctions and recover from them. Research Purpose: The purpose of this research is investigating diverse mitigation strategies adopted by retail organizations in response to non-consumer AI malfunctions, and to construct a comprehensive framework to address this. Method: This research adopts an exploratory qualitative approach, utilizing semi-structured interviews to gather in-depth insights. Data was collected from key personnel across various retail companies, employing inductive coding using the Gioia Methodology Theory, to identify and analyse emerging themes. In total, interview data from 12 retail specialists was collected and analysed. This methodology is grounded in Sociotechnical Systems Theory, capturing the complex interplay between social and technical factors, ensuring a comprehensive understanding of the strategic responses of retail organizations to AI malfunctions. Conclusion: Our study contributes to various research gaps, as it is the first comprehensive study on non-consumer AI malfunctions in the retail industry. By developing a comprehensive framework for addressing non-consumer AI malfunctions, the study contributes to the ongoing advancement of knowledge and understanding in this important area of study. Our study extends the theoretical understanding of the interplay between technology, organization, and strategies, highlighting their inseparable relationship when addressing non-consumer AI malfunctions. Our findings reveal that effective management of AI malfunctions requires adjustments in both social and technical systems, emphasizing the importance of organizational resilience and adaptability.

Efficient production scheduling is important for both maximizing productivity and minimizing costs in manufacturing environments. This thesis presents an approach to optimizing production scheduling using Artificial Intelligence (AI) and Genetic Algorithms (GAs). The primary goal is to develop a generalized solution that can be modified and adapted to the varying needs that different production- or manufacturing lines may have. This research has two main research questions that address the problem at hand. (1) Can Genetic Algorithms be used to optimize a sequence of products in a production line? and (2) How effectively can Genetic Algorithms optimize the sequencing of production tasks in diverse production lines to minimize total order processing times? Through experimentation with various GA configurations the results achieved suggested that GAs were appropriate for sequence optimization. The study demonstrates that GAs can optimize a production line up to almost 42% , which significantly reduced the total processing time. The thesis also highlights the importance of the representation of data, which is essential for the optimization of the sequence.

Syftet med studien är att bidra med ökad kunskap om gymnasieelevers erfarenheter och upplevelser av den digitala teknikutvecklingen, inklusive artificiell intelligens och algoritmiska tjänster, och hur elever formerar sina tankar om vilka konsekvenser det får för samhällskunskapsundervisningen och det framtida demokratiska deltagandet. För att uppfylla syftet har semistrukturerade samtalsintervjuer använts som datainsamlingsmetod där totalt elva elever intervjuats från två olika skolor. En öppen tematisk analys applicerades på datan som resulterade i åtta olika teman. Därefter analyserades resultatet utifrån Andrew Feenbergs teoretiska referensram som består av en kritisk förståelse för hur teknologi och dess utveckling formar och påverkar samhället och hur människor interagerar med den. Eleverna kom fram till att AI kan användas för att effektivisera industrin likväl som ett verktyg i skolan för att hjälpa elever med inlärningen. Däremot har AI negativa sidor också. Rädslan att AI tar över mänskligheten växer och det skapas innehåll som är deepfake där det är svårt att avgöra vad som stämmer. Algoritmer kan också bli ett problem då det många gånger baseras på vad du tittar på och att det blir som en konfirmeringsbias. Du får bara upp ett fåtal perspektiv. Elevernamenar att människor utnyttjas av producenterna av AI och algoritmer eftersom det styr oss i en viss riktning. Eleverna kommer fram till att det måste finnas någon kontroll för att inte kränka individer och skada demokratin och ger förslag på en statlig eller överstatlig kontroll. AI har även lett till att eleverna får en försvårad skolgång med fler prov. Tilliten mellan lärare ochelever minskar. AI måste integreras mer i skolan. Därtill menar eleverna att nya kunskaper kommer bli viktiga, däribland tekniska kunskaper, källkritik, öppenhet och förmågor så som att resonera och argumentera. Eleverna har en önskan om att de ska förberedas med de nya kunskaperna för att aktivt kunna ta del av det nya samhället som komma skall. / The purpose of the study is to contribute with knowledge about high school students’ experiences of the development of digital technology, including artificial intelligence and algorithms. The study aims to explain how the development of digital technologies might change the subject social studies in school and a future democratic citizenship. Semi-structuredconversational interviews have been used as a data collection method where a total of eleven students have been interviewed from two different schools. An open thematic analysis was used which resulted in eight themes. The results were analyzed based on Andrew Feenberg’s theory, which consist of a critical understanding of how technology and its development shape and influence society and how people interact with it. The students concluded that AI can be used as a tool to help students with learning. However, students felt a rising fear that AI might wipe out humanity and the fact that it can create deepfake content which makes it difficult to determine what is true. Algorithms, like your for-u-page can lead to a narrowed perspectivewhere you do not see the bigger picture. Furthermore, the students believe that humans are exploited by the producers of AI and platforms that uses algorithms since it steers us in a certain direction. The students concluded that AI needs to be controlled, for example by the state. They think that might avoid violations and a restriction of democracy. The development of AI hasled to an increase in tests for the students. The trust between teachers and students have decreased. The students wish to talk more about AI and the effects of it. They also believe that a new set of competences will become more important, for example technical knowledge, how to deal with a big amount of information as well as abilities such as reasoning and arguing, to be able to actively take part in the new society that is yet to come.

[ES] La aplicación de la inteligencia artificial (IA) en reproducción asistida aborda el complejo panorama de la infertilidad, una patología prevalente que afecta a un porcentaje significativo de la población en edad reproductiva. Los avances en medicina reproductiva, marcados por hitos como la fecundación in vitro (FIV) y la microinyección intracitoplasmática de espermatozoides (ICSI), han dado lugar al desarrollo de técnicas de reproducción asistida (TRA). Aunque la transferencia múltiple de embriones (MET) se ha empleado tradicionalmente para aumentar las posibilidades de embarazo, conlleva riesgos. Por ello, las técnicas de selección embrionaria han despertado un creciente interés. La introducción de incubadores con tecnología time-lapse permitió analizar embriones sin alterar las condiciones de cultivo y supuso la introducción de los primeros algoritmos de selección embrionaria. En consecuencia, desarrollar e incluir enfoques de IA es el reto actual. Esta tesis aborda retos del mundo real en el campo de la embriología mediante la aplicación de métodos de aprendizaje profundo. El objetivo final es diseñar, desarrollar y validar herramientas que apoyen la rutina diaria en un laboratorio de FIV, mejorando en última instancia las tasas de éxito en las clínicas de reproducción asistida. La complejidad de las tareas resueltas aumenta sistemáticamente, proporcionando un conocimiento consistente basado en la embriología. Los objetivos específicos consisten en resolver tareas concretas con diferentes metodologías y explorar técnicas novedosas de IA. Las tareas incluyen la fecundación, la viabilidad, la calidad y la predicción de euploides. Los enfoques técnicos abarcan la automatización, segmentación, aprendizaje contrastivo supervisado y técnicas de transferencia inductiva. Los resultados contribuyen al campo de la embriología, mostrando aplicaciones potenciales de metodologías innovadoras de IA. Los objetivos futuros introducen una integración coherente en los laboratorios de embriología, teniendo en cuenta las condiciones clínicas reales, contribuir a mejorar las tasas de éxito en las clínicas de reproducción asistida, y explorar en mayor profundidad técnicas no-invasivas para el análisis genético. / [CA] L'aplicació de la intel·ligència artificial (IA) en reproducció assistida aborda el complex panorama de la infertilitat, una patologia prevalent que afecta un percentatge significatiu de la població en edat reproductiva. Els avanços en medicina reproductiva, marcats per fites com la fecundació in vitro (FIV) i la microinjecció intracitoplasmàtica d'espermatozoides (ICSI), han donat lloc al desenvolupament de tècniques de reproducció assistida (TRA). Encara que la transferència múltiple d'embrions (MET) s'ha emprat tradicionalment per a augmentar les possibilitats d'embaràs, comporta riscos. Per això, les tècniques de selecció embrionària han despertat un creixent interés. La introducció d'incubadors amb tecnologia time-lapse va permetre analitzar embrions sense alterar les condicions de cultiu i va suposar la introducció dels primers algorismes de selecció embrionària. En conseqüència, desenvolupar i incloure enfocaments de IA és el repte actual. Esta tesi aborda reptes del món real en el camp de l'embriologia mitjançant l'aplicació de mètodes d'aprenentatge profund. L'objectiu final és dissenyar, desenvolupar i validar eines que donen suport a la rutina diària en un laboratori de FIV, millorant en última instància les taxes d'èxit en les clíniques de reproducció assistida. La complexitat de les tasques resoltes augmenta sistemàticament, proporcionant un coneixement consistent basat en l'embriologia. Els objectius específics consistixen a resoldre tasques concretes amb diferents metodologies i explorar tècniques noves de IA. Les tasques inclouen la fecundació, la viabilitat, la qualitat i la predicció d'euploides. Els enfocaments tècnics inclouen automatització, segmentació, aprenentatge contrastiu supervisat i tècniques de transferència inductiva. Els resultats contribuïxen al camp de l'embriologia, mostrant aplicacions potencials de metodologies innovadores de IA. Els objectius futurs introduïxen una integració coherent en els laboratoris d'embriologia, tenint en compte les condicions clíniques reals, contribuir a millorar les taxes d'èxit en les clíniques de reproducció assistida, i explorar en major profunditat tècniques no-invasives per a l'anàlisi genètica / [EN] The application of artificial intelligence (AI) in assisted reproduction addresses the complex landscape of infertility, a prevalent condition affecting a significant percentage of the reproductive-age population. Advances in reproductive medicine, marked by milestones such as in vitro fertilization (IVF) and intracytoplasmic sperm microinjection (ICSI), have led to the development of assisted reproduction techniques (ART). While multiple embryo transfer (MET) has traditionally been employed to increase pregnancy chances, it carries risks. Therefore, embryo selection techniques have suffered a rapid increase in interest. The introduction of incubators with time-lapse technology allowed embryo analysis without disturbing culture conditions and involved the introduction of the first embryo selection algorithms. Consequently, developing and including AI approaches is the current challenge. This thesis addresses real-world challenges in the embryology field by applying deep learning methods. The final goal is to design, develop, and validate tools that support the daily routine in an IVF laboratory, ultimately improving success rates in assisted reproductive clinics. The complexity of the solved tasks increases systematically, providing consistent knowledge based on embryology. Specific goals involve solving concrete tasks with different methodologies and exploring novel AI techniques. The tasks include fecundation, viability, quality, and prediction of euploid embryos. The technical approaches encompass automation, segmentation, supervised contrastive learning, and inductive transfer techniques. The findings contribute to the field of embryology, showcasing potential applications of innovative AI methodologies. Future goals introduce consistent integration into embryology laboratories, taking into account real clinical conditions, contributing to improved success rates in assisted reproduction clinics, and further exploring non-invasive techniques for genetic analysis. / Paya Bosch, E. (2024). Deep Learning for the Automation of Embryo Selection in an In Vitro Fertilization Laboratory [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/206839

Cette thèse aborde la problématique de la création d’intelligences artificielles (IA) contrôlant la prise de décision haut-niveau dans les jeux de stratégie. Ce type de jeux propose des environnements complexes nécessitant de manipuler de nombreuses ressources en faisant des choix d’actions dépendant d’objectifs à long terme. La conception de ces IA n’est pas simple car il s’agit de fournir une expérience pour le joueur qui soit divertissante et intéressante à jouer. Ainsi, le but n’est pas d’obtenir des comportements d’IA imbattables, mais plutôt de refléter différents traits de personnalités permettant au joueur d’être confronté à des adversaires diversifiés. Leur conception fait intervenir des game designers qui vont définir les différentes stratégies en fonction de l’expérience qu’ils souhaitent créer pour le joueur, et des développeurs qui programment et intègrent ces stratégies au jeu. La collaboration entre eux nécessite de nombreux échanges et itérations de développement pour obtenir un résultat qui correspond aux attentes des designers. L’objectif de cette thèse est de proposer une solution de modélisation de stratégies accessible aux game designers en vue d’améliorer et de simplifier la création de comportements stratégiques. Notre proposition prend la forme d’un moteur stratégique choisissant des objectifs à long terme et vient se placer au dessus d’un module tactique qui gère l’application concrète de ces objectifs. La solution proposée n’impose pas de méthode pour résoudre ces objectifs et laisse libre le fonctionnement du module tactique. Le moteur est couplé à un modèle de stratégie permettant à l’utilisateur d’exprimer des règles permettant au moteur de choisir les objectifs et de leur allouer des ressources. Ces règles permettent d’exprimer le choix d’objectifs en fonction du contexte, mais également d’en choisir plusieurs en parallèle et de leur donner des importances relatives afin d’influencer la répartition des ressources. Pour améliorer l’intelligibilité nous utilisons un modèle graphique inspiré des machines à états finis et des behavior trees. Les stratégies créées à l’aide de notre modèle sont ensuite exécutées par le moteur de stratégie pour produire des directives qui sont données au module tactique. Ces directives se présentent sous la forme d’objectifs stratégiques et de ressources qui leur sont allouées en fonction de leurs besoins et de l’importance relative qui leur a été donnée. Le module stratégique permet donc de rendre accessible la conception du niveau stratégique d’une IA contrôlant un adversaire dans un jeu de stratégie. / This PhD thesis addresses the topic of creating artificial intelligence (AI) to control high-level decision-making in strategy games. This kind of game offers complex environments that require the manipulation of a large number of resources by choosing actions depending on long-term goals. This AI design is not simple because it is about providing to the player a playful and interesting experience. Hence, the aim is not to create unbeatable behaviors, but rather to display several personality traits allowing the player to face diverse opponents. Its creation involves game designers who are responsible of defining several strategies according to the experience they want to provide to the player, and game developers who implement those strategies to put them into the game. The collaboration between them requires many exchanges and development iterations to obtain a result corresponding to game designers’ expectations. The objective of this PhD thesis is to improve and simplify the creation of strategical behaviors by proposing a strategy model intelligible to game designers and that can be interfaced easily with developers’ work. For game designers, a strategy model has been created to allow them to express rules guiding the choice of goals and their allocated resources. These rules make it possible for game designers to express which goal to choose according to the context but also to choose several of them and give them relative importance in order to influence the resource distribution. To improve intelligibility we use a graphical model inspired from finite state machines and behavior trees. Our proposition also includes a strategy engine which executes the strategies created with the model. This execution produces directives that are represented by a list of selected strategical goals and the resources that have been allocated according to the importance and needs of each goal. These directives are intended for a tactical module in charge of their application. The developers are then responsible for the implementation of this tactical module. Our solution enables game designers to directly design the strategical level of an AI and therefore facilitates their cooperation with game developers and simplifies the entire creation process of the AI.

Network densification has become a dominant theme for capacity enhancement in cellular networks. However, it increases the operational complexity and expenditure for mobile network operators. Consequently, the essential features of Self-Organising Networks (SON) are considered to ensure the economic viability of the emerging cellular networks. This thesis focuses on quantifying the benefits of self-organisation in Cloud Radio Access Network (C-RAN) by proposing a flexible, energy efficient, and capacity optimised system. The Base Band Unit (BBU) and Remote Radio Head (RRH) map is formulated as an optimisation problem. A self-optimised C-RAN (SOCRAN) is proposed which hosts Genetic Algorithm (GA) and Discrete-Particle-Swarm-Optimisation algorithm (DPSO), developed for optimisation. Computational results based on different network scenarios demonstrate that DPSO delivers excellent performances for the key performance indicators compared to GA. The percentage of blocked users is reduced from 10.523% to 0.409% in a medium sized network scenario and 5.394% to 0.56% in a vast network scenario. Furthermore, an efficient resource utilisation scheme is proposed based on the concept of Cell Differentiation and Integration (CDI). The two-stage CDI scheme semi-statically scales the number of BBUs and RRHs to serve an offered load and dynamically defines the optimum BBU-RRH mapping to avoid unbalanced network scenarios. Computational results demonstrate significant throughput improvement in a CDI-enabled C-RAN compared to a fixed C-RAN, i.e., an average throughput increase of 45.53% and an average blocked users decrease of 23.149% is experienced. A power model is proposed to estimate the overall power consumption of C-RAN. Approximately 16% power reduction is calculated in a CDI-enabled C-RAN when compared to a fixed C-RAN, both serving the same geographical area. Moreover, a Divide-and-Sort load balancing scheme is proposed and compared to the SOCRAN scheme. Results show excellent performances by the Divide-and-Sort algorithm in small networks when compared to SOCRAN and K-mean clustering algorithm.

Made available in DSpace on 2016-04-29T14:23:49Z (GMT). No. of bitstreams: 1 Luciana Akemi Nakabayashi.pdf: 390381 bytes, checksum: a5f04f61e556536db3c6dfab97227bb0 (MD5) Previous issue date: 2009-05-12 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The current research has as central theme investigate the concept of intelligence, and specifically of Artificial Intelligence (AI), derived from the Computer Science and its effect on research of Philosophy of Mind. It tries to consider and to understand such perspective to the light of Technoscience and Cybernetics, in view of the concepts that look for to understanding the human mind by the imitation of its behavior, applying the concepts to the investigation and simulation of dialogues: called Chatterbots. The research stars from a methodological and bibliographic study diverse from the concepts of Artificial Intelligence (AI) and the concepts of Philosophy of Mind, focusing on the prominence of the issue in its interdisciplinary aspects. Discusses the prospects consolidated in the community about the issue, especially the approaches of Searle (1984, 1992, 1997), Gardner (1994, 1995) and, in the Brazilian landscape, from Teixeira (1990, 1994, 1995, 1998, 2000, 2004, 2008), focusing on relations between the concepts of intent, brain, mental and cognitive experiment, as well as some trends of criticism and defense of its formal limits. Drawing up of previous searches on the subject and methodology that merges computing elements and philosophy of mind, analyzes the conceptual experiment of the Chinese Room of Searle (in aspects of syntax and semantics). From this experiment and its results, it presents the prospect of Artificial Intelligence (AI) investigation as belonging to the nature of cognition, supported by the theory of cognition, particularly for phenomena such as categorization and identification of objects, problem solving, decision and conscience. The search culminates in the analysis of the concept of Artificial Intelligence (AI) in Philosophy of Mind, proposed as a noematic element, being constituted as prerequisite for the production of scientific knowledge, implemented in the areas of Intelligence Technology and Digital Design. Applies the concepts and achievements to the definition and extension of the concept of hypertext, taking it as a computational mechanism capable of structuring hierarchical dialogues, indexed, so as to the formulation of Robot Primo (2001) and Roth Coelho (2001). Finally, the assumptions investigated are shown in results observed in the so-called Chatterbots in the area of Artificial Intelligence (AI), highlighting its characteristics and its importance in the current context of its computer utilization in cyberspace / A presente pesquisa tem como tema central investigar o conceito de Inteligência e, especificamente, de Inteligência Artificial (IA), derivado das Ciências da Computação e sua repercussão nas pesquisas da Filosofia da Mente. Procura conceituar e entender tal perspectiva à luz da Tecnociência e da Cibernética, tendo em vista os conceitos que buscam compreender a mente humana pela imitação de seu comportamento, aplicando os conceitos à investigação e simulação de diálogos: os chamados chatterbots. A pesquisa parte de um estudo metodológico e bibliográfico diversificado dos conceitos de Inteligência Artificial (IA) e das concepções de Filosofia da Mente, enfocando a proeminência do tema em seus aspectos interdisciplinares. Discute as perspectivas consolidadas na comunidade acerca do tema, especialmente as abordagens de Searle (1984, 1992, 1997), Gardner (1994, 1995) e, no panorama brasileiro, de Teixeira (1990, 1994, 1995, 1998, 2000, 2004, 2008), incidindo nas relações entre os conceitos de intencionalidade, cérebro, experimento mental e cognitivismo, bem como algumas tendências críticas e de contestação de seus limites formais. Valendo-se de pesquisas anteriores sobre o tema e de metodologia que mescla elementos computacionais e de filosofia da mente, analisa o experimento conceitual do quarto chinês de Searle (em seus aspectos de sintaxe e semântica). A partir deste experimento e seus resultados, apresenta a perspectiva da investigação da Inteligência Artificial (IA), como pertencente à natureza da cognição, apoiada na teoria da cognição, nomeadamente de fenômenos como categorização e identificação de objetos, resolução de problemas, decisão e consciência. A pesquisa culmina na análise do conceito de Inteligência Artificial (IA), na Filosofia da Mente, proposto como um elemento noemático, constituindo-se como condição para a produção do conhecimento científico, aplicado este nas áreas de Tecnologia da Inteligência e Design Digital. Aplica os conceitos e resultados alcançados à definição e extensão do conceito de hipertexto, tomando-o como um mecanismo computacional capaz de estruturar diálogos hierarquizados, indexados, ao modo da formulação de Robot de Primo (2001) e Roth Coelho (2001). Por fim, os pressupostos investigados são apresentados em resultados observados nos chamados chatterbots na área de Inteligência Artificial (IA), destacando suas características e sua importância no atual contexto de sua utilização computacional no ciberespaço

Nos travaux s’inscrivent dans le cadre de la formation à la gestion de crise en environnements virtuels. La scénarisation joue un rôle essentiel pour l’apprentissage humain en environnement virtuel. Cela permet à la fois de proposer et d’orchestrer des situations d’apprentissage personnalisées et également d’amener l’apprenant vers des scénarios pertinents et formateurs. Les travaux présentés dans cette thèse s’intéressent à la génération dynamique de scénarios et à leur exécution en environnements virtuels. Pour cette scénarisation, nous visons un ensemble d’objectifs qui sont souvent contradictoires : la liberté d’action de l’utilisateur, la génération de scénarios variés et fidèles à l’intention de l’auteur, le contrôle scénaristique et la résilience du système de scénarisation. Les différentes approches de la narration interactive favorisent plus ou moins certains de ces objectifs mais il est difficile de tous les concilier, et c’est là l’enjeu de nos travaux. En plus de ces objectifs, nous cherchons également à faciliter la modélisation du contenu scénaristique qui est encore de nos jours un réel enjeu lorsqu’il s’agit de scénariser des environnements complexes comme celui de la gestion de crise. Nous proposons une approche émergente dont le scénario vécu par l’apprenant va émerger des interactions entre l’apprenant, les personnages virtuels et notre système de scénarisation MENTA. MENTA est chargé du contrôle scénaristique en proposant un ensemble d’ajustements (sur la simulation) répondant à des objectifs scénaristiques choisis par le formateur (p. ex., faire travailler certaines compétences en particulier). Ces ajustements prennent la forme d’un scénario prescrit qui est généré par MENTA via un moteur de planification que nous avons couplé avec des cartes cognitives floues au travers d’un macro-opérateur FRAG. Un FRAG permet de modéliser des fragments de scénarios sous la forme de séquence d’actions/événements scriptés. L’originalité de notre approche repose sur un couplage fort entre planification et modèles graphiques qui permet de conserver les propriétés d’exploration et de puissance générative d’un moteur de planification (ce qui favorise la variabilité et la résilience du système), tout en facilitant la modélisation du contenu scénaristique ainsi que l’intention de l’auteur au travers de morceaux de scénario qui vont être scriptés par l’auteur et réutilisés dans la planification. Nous avons travaillé sur un exemple applicatif concret de scénarios portant sur la gestion d’un afflux massif de blessés, puis nous avons implémenté MENTA et généré des scénarios relatifs à cet exemple. Enfin, nous avons testé et analysé les performances de notre système. / Our work is related to the training of crisis management in virtual environments. The specification of possible unfoldings of events in a simulation is essential for human learning in a virtual environment. This allows both to propose and orchestrate personalized learning situations and also to bring the learner toward relevant and educative scenarios. The work presented in this thesis focuses on the dynamic generation of scenarios and their execution in a virtual environment. For that, we aim at a set of objectives that are often contradictory : the freedom of action of the user, the generation of various scenarios that respect the authorial intent, the narrative control and the capacity of the system to adapt to deviations fromthe learner. The different approaches of interactive storytelling tackle more or less some of these objectives, but it is difficult to satisfy them all, and this is the challenge of our work. In addition to these objectives, we also aim at facilitating the modeling of the narrative content, which is still a real issue today when it comes to model complex environments such as the ones related to crisis management. We propose an emergent approachwhere the scenario experienced by the learner will emerge fromthe interactions between the learner, the virtual characters and our narrative system MENTA. MENTA is in charge of the narrative control by proposing a set of adjustments (over the simulation) that satisfies narrative objectives chosen by the trainer (e. g., a list of specific skills). These adjustments take the form of a prescribed scenario that is generated by MENTA via a planning engine that we have coupled with fuzzy cognitive maps through a macro-operator FRAG. A FRAG is used to model FRAGment of scenario in the form of scripted sequences of actions/events. The originality of our approach relies on a strong coupling between planning and graphical models which preserves the exploration capability and the generative power of a planning engine (which contributes to the generation of various and adaptable scenarios), while facilitating the modeling of narrative content as well as the authorial intent thanks to fragments of scenario that are scripted by the author and used during the planning process. We have worked on a concrete application example of scenarios dealing with the management of a massive influx of victims. Then, we have implemented MENTA and generated scenarios related to this example. Finally, we have tested and analyzed the performance of our system.