<p align="justify">Computer Science is currently facing a grand challenge :finding good design practices for embedded systems. Embedded systems are essentially computers interacting with some physical process. You could find one in a braking systems or in a nuclear power plant for example. They present several design difficulties :first they are reactive systems, interacting indefinitely with their environment. Second,they must satisfy real-time constraints specifying when they should respond, and not only how. Finally, their environment is often deeply continuous, presenting complex dynamics. The formal models of choice for specifying such systems are timed and hybrid automata for which model checking is pretty well studied.</p> <p><p align="justify">In a first part of this thesis, we study a complete design approach, including verification and code generation, for timed automata. We have to define a new semantics for timed automata, the AASAP semantics, that preserves the decidability properties for model checking and at the same time is implementable. Our notion of implementability is completely novel, and relies on the simulation of a semantics that is obviously implementable on a real platform. We wrote tools for the analysis and code generation and exemplify them on a case study about the well known Philips Audio Control Protocol.</p> <p><p align="justify">In a second part of this thesis, we study the problem of controller synthesis for an environment specified as a hybrid automaton. We give a new solution for discrete controllers having only an imperfect information about the state of the system. In the process, we defined a new algorithm, based on the monotonicity of the controllable predecessors operator, for efficiently finding a controller and we show some promising applications on a classical problem :the universality test for finite automata. / Doctorat en sciences, Spécialisation Informatique / info:eu-repo/semantics/nonPublished

This work concentrates on the study of mathematical models of human behaviour in dynamic games; in particular games with aspects of cooperation, implementation of a model and experimentation with the model. The game DarkElf was chosen for this project. It is a strategic, turn based game with economic and military features, where the decisions made by players are simultaneously implemented at a predetermined time.

L’apprentissage adaptatif distribué sur les réseaux permet à un ensemble d’agents de résoudre des problèmes d’estimation de paramètres en ligne en se basant sur des calculs locaux et sur des échanges locaux avec les voisins immédiats. La littérature sur l’estimation distribuée considère essentiellement les problèmes à simple tâche, où les agents disposant de fonctions objectives séparables doivent converger vers un vecteur de paramètres commun. Cependant, dans de nombreuses applications nécessitant des modèles plus complexes et des algorithmes plus flexibles, les agents ont besoin d’estimer et de suivre plusieurs vecteurs de paramètres simultanément. Nous appelons ce type de réseau, où les agents doivent estimer plusieurs vecteurs de paramètres, réseau multitâche. Bien que les agents puissent avoir différentes tâches à résoudre, ils peuvent capitaliser sur le transfert inductif entre eux afin d’améliorer les performances de leurs estimés. Le but de cette thèse est de proposer et d’étudier de nouveaux algorithmes d’estimation distribuée sur les réseaux multitâches. Dans un premier temps, nous présentons l’algorithme diffusion LMS qui est une stratégie efficace pour résoudre les problèmes d’estimation à simple-tâche et nous étudions théoriquement ses performances lorsqu’il est mis en oeuvre dans un environnement multitâche et que les communications entre les noeuds sont bruitées. Ensuite, nous présentons une stratégie de clustering non-supervisé permettant de regrouper les noeuds réalisant une même tâche en clusters, et de restreindre les échanges d’information aux seuls noeuds d’un même cluster / Distributed adaptive learning allows a collection of interconnected agents to perform parameterestimation tasks from streaming data by relying solely on local computations and interactions with immediate neighbors. Most prior literature on distributed inference is concerned with single-task problems, where agents with separable objective functions need to agree on a common parameter vector. However, many network applications require more complex models and flexible algorithms than single-task implementations since their agents involve the need to estimate and track multiple objectives simultaneously. Networks of this kind, where agents need to infer multiple parameter vectors, are referred to as multitask networks. Although agents may generally have distinct though related tasks to perform, they may still be able to capitalize on inductive transfer between them to improve their estimation accuracy. This thesis is intended to bring forth advances on distributed inference over multitask networks. First, we present the well-known diffusion LMS strategies to solve single-task estimation problems and we assess their performance when they are run in multitask environments in the presence of noisy communication links. An improved strategy allowing the agents to adapt their cooperation to neighbors sharing the same objective is presented in order to attain improved learningand estimation over networks. Next, we consider the multitask diffusion LMS strategy which has been proposed to solve multitask estimation problems where the network is decomposed into clusters of agents seeking different

We study games where a team of agents needs to collaborate against an adversary to achieve a common goal. The agents make their moves simultaneously, and they have different perceptions about the system state after each move, due to different sensing capabilities. Each agent can only act based on its own experiences, since no communication is assumed during the game. However, before the game begins, the agents can agree on some strategy. A strategy is winning if it guarantees that the agents achieve their goal regardless of how the opponent acts. Identifying a winning strategy, or determining that none exists, is known as the strategy synthesis problem. In this thesis, we only consider a simple objective where the agents must force the game into a given state. Much of the literature is focused on strategies that either rely on that the agents (a) can remember everything that they have perceived or (b) can only remember the last thing that they have perceived. The strategy synthesis problem is (in the general case) undecidable in (a) and has exponential running time in (b). We are interested in the middle, where agents can have finite memory. Specifically, they should be able to keep a finite-state machine, which they update when they make new observations. In our case, the internal state of each agent represents its knowledge about the state of affairs. In other words, an agent is able to update its knowledge, and act based on it. We propose an algorithm for constructing the finite-state machine for each agent, and assigning actions to the internal states before the game begins. Not every winning strategy can be found by the algorithm, but we are convinced that the ones found are valid ones. An important building block for the algorithm is the knowledge-based subset construction (KBSC) used in the literature, which we generalise to games with multiple agents. With our construction, the game can be reduced to another game, still with uncertain state information, but with less or equal uncertainty. The construction can be applied arbitrarily many times, but it appears as if it stabilises (so that no new knowledge is gained) after only a few steps. We discuss this and other interesting properties of our algorithm in the final chapters of this thesis. / Vi studerar spel där ett lag agenter behöver samarbeta mot en motståndare för att uppnå ett mål. Agenterna agerar samtidigt, och vid varje steg av spelet så har de olika uppfattning om spelets tillstånd. De antas inte kunna kommunicera under spelets gång, så agenterna kan bara agera utifrån sina egna erfarenheter. Innan spelet börjar kan agenterna dock komma överrens om en strategi. En sådan strategi är vinnande om den garanterar att agenterna når sitt mål oavsett hur motståndaren beter sig. Att hitta en vinnande strategi är känt som syntesproblemet. I den här avhandlingen behandlar vi endast ett enkelt mål där agenterna måste tvinga in spelet i ett givet tillstånd. Mycket av litteraturen handlar om strategier där agenterna antingen antas (a) kunna minnas allt som de upplevt eller (b) bara kunna minnas det senaste de upplevt. Syntesproblemet är (i det generella fallet) oavgörbart i (a) och tar exponentiell tid i (b). Vi är intressede av fallet där agenter kan ha ändligt minne. De ska kunna ha en ändlig automat, som de kan uppdatera när de får nya observationer. I vårt fall så representerar det interna tillståndet agentens kunskap om spelets tillstånd. En agent kan då uppdatera sin kunskap och agera utifrån den. Vi föreslår en algoritm som konstruerar en ändlig automat åt varje agent, samt instruktioner för vad agenten ska göra i varje internt tillstånd. Varje vinnande strategi kan inte hittas av algoritmen, men vi är övertygade om att de som hittas är giltiga. En viktig byggsten är den kunskapsbaserade delmängskonstruktionen (KBSC), som vi generaliserar till spel med flera agenter. Med vår konstruktion kan spelet reduceras till ett annat spel som har mindre eller lika mycket osäkerhet. Detta kan göras godtyckligt många gånger, men det verkar som om att ingen ny kunskap tillkommer efter bara några gånger. Vi diskuterar detta vidare tillsammans med andra intressanta egenskaper hos algoritmen i de sista kapitlen i avhandlingen.