Cette thèse est consacrée à une étude d'un point de vue général de jeux d'accessibilité dans des systèmes munis de compteurs. Dans ce type de jeux, l'objectif de l'un des deux joueurs est d'atteindre une configuration particulière, qui est composée d'un sommet de l'arène où le jeu se déroule et d'un n-uplet de valeurs pour les compteurs. Ces valeurs de compteurs sont mises à jour, généralement par des additions de vecteurs, lorsqu’un arc est emprunté. Le problème de décision associé à un jeu d’accessibilité est de savoir si le joueur en question a une stratégie gagnante depuis une configuration donnée. Lorsque ce problème est décidable, on s’intéresse à la possibilité de décrire l’ensemble de ces configurations dites gagnantes. Au cours de l’étude, des caractéristiques des jeux d’accessibilités avec des compteurs sont mises en parallèle, en cherchant des similarités, ou au contraire des différences, au niveau de la décidabilité et de la complexité du problème de décision, quand l’une de ces caractéristiques est modifiée. On retiendra en tant que caractéristique majeure le comportement quand un compteur devrait devenir négatif. Nous nous focalisons principalement sur trois sémantiques. Nous considérons également d’autres caractéristiques selon lesquelles il était possible de comparer décidabilité et complexité. Nous nous penchons sur un modèle intitulé « robot games », sur lequel nous obtenons des résultats majeurs : un algorithme de complexité asymptotiquement optimale en dimension un et une preuve d’indécidabilité en dimension trois. / This thesis is devoted to a general study of a reachability games on systems with counters. In this kind of games, the objective of one of two players is to reach a particular configuration, which is a pair composed of a vertex of the game arena and a tuple of values for the counters. The values of the counters are updated, usually by vector additions, when a edge is taken. The decision problem associated with a reachability game is whether a player has a winning strategy for the game from a given configuration, in other words whether the configuration is winning. When the problem of determining the winner from a given configuration is decidable, we wonder whether it is even possible to describe the set of winning configurations. In our study, we look at various features of counter reachability games, finding similarities or, on the contrary, differences with regard to decidability or complexity of the decision problem, when one of the features is modified. The main feature that we consider is what happens when a counter should become negative. We focus primarily on three semantics. We also consider other features that allow to compare decidability and complexity. We introduce a model, called “robot games”, on which we obtain our main results: an algorithm with an optimal complexity for dimension one, and undecidability for dimension three.

To achieve a high level of reliability, efficiency, and robustness in electric systems, the concept of smart grid has been proposed. It is an update of the traditional electric grid designed to meet current and future customers' requirements. With the smart grid, demand management has been adopted in order to shape the load pattern of the consumers, maintain supply-demand balance, and reduce the total energy cost. In this thesis, we focus mainly on energy savings by critically investigating the problem of load management in the smart grid. We first propose a user aware demand management approach that manages residential loads while taking into consideration users' comfort. This latter is modeled in a simple yet effective way that considers waiting time, type of appliance, as well as a weight factor to prioritize comfort or savings. The proposed approach is based on game theory using a modified regret matching procedure. It provides users with high incentives to participate actively in load management and borrows advantages of both centralized and decentralized schemes. Then, we investigate the issue of fairness within demand response programs. The fair division of the system bill stemming from the use of shared microgrid resources with different costs is examined. The Shapley value provides one of the core solutions to fairness problems; however, it has been known to be computationally expensive for systems such as microgrids. Therefore, we incorporate an approximation of the Shapley value into a demand response algorithm to propose a fair billing mechanism based on the contribution of each user towards attaining the aggregated system cost. Finally, we study energy trading in the smart grid as an alternative way to reduce the load on the grid by efficiently using renewable energy resources. We propose a solution that takes into account the smart grid physical infrastructure, in addition to the distribution of its users. Different constraints stemming from the nature of the smart grid have been considered towards a realistic solution. We show through simulation results that all of the proposed schemes reduce the load on the grid, the energy bills, and the total system energy cost while maintaining the users' comfort as well as fairness.

The thesis analyses Czech and EU legislation which is intended to prevent discrimination in the labour market and focuses on the three main models of discrimination: Discriminatory preference, Statistical discrimination and Displacing discrimination. In the opening chapters, the thesis presents the reasons leading to the differences in men's and women's wages not originating from discrimination and also partially disproves the myths about discrimination of women. The analytical and theoretical part is followed by empirical part whose conclusions do not disprove the hypothesis of discrimination in Czech labour market, nevertheless comes to the conclusion that the part of the differences in average income, that exist, can be explained mainly by following factors: the number of worked hours, education level, time spent mainly by women on maternity / parental leave and child care and other factors. The results of questionnaire survey realised on the territory of one region and within one area of intervention of Structural funds confirm that the discrimination between men and women doesn't exist.

This thesis will enable the reader to see how applications of game theory can help understand the development in oil market. Firstly, the thesis will introduce to the reader the foundations of game theory, a branch of microeconomics studying strategic interaction. Special focus will be put on the theory of oligopoly, the most important collusive and non-collusive models of oligopoly and their similarities and differences. Secondly, the thesis will show the reader how these theoretic models can be applied to the past development within the global oil industry, most prominently on the Organization of the Petroleum Exporting Countries. Finally, the thesis will present possible applications of game theory and some of the models to the oil market after the price decrease which started in 2014. This would help the reader assess the potential outcomes of the current crisis and possible development in the future.

In the study of Banach spaces, the development of some key properties require studying topologies on the collection of closed convex subsets of the space. The subcollection of closed linear subspaces is studied under the relative slice topology, as well as a class of topologies similar thereto. It is shown that the collection of closed linear subspaces under the slice topology is homeomorphic to the collection of their respective intersections with the closed unit ball, under the natural mapping. It is further shown that this collection under any topology in the aforementioned class of similar topologies is a strong Choquet space. Finally, a collection of category results are developed since strong Choquet spaces are also Baire spaces.

A big challenge in artificial intelligence (AI) is creating autonomous agents that can interact well with other agents over extended periods of time. Most previously developed algorithms have been designed in the context of Repeated Games, environments in which the agents interact in the same scenario repeatedly. However, in most real-world interactions, relationships between people and autonomous agents consist of sequences of distinct encounters with different incentives and payoff structures. Therefore, in this thesis, we consider Interaction Games, which model interactions in which the scenario changes from encounter to encounter, often in ways that are unanticipated by the players. For example, in Interaction Games, the magnitude of payoffs as well as the structure of these payoffs can differ across encounters. Unfortunately, while there have been many algorithms developed for Repeated Games, there are no known algorithms for playing Interaction Games. Thus, we have developed two different algorithms, augmented Fictitious Play (aFP) and augmented S# (Aug-S#), for playing these games. These algorithms are designed to generalize Fictitious Play and S# algorithms, which were previously created for Repeated Games, to the more general kinds of scenarios modeled by Interaction Games. This thesis primarily focuses on the evaluation of these algorithms. We first analyze the behavioral and performance properties of these algorithms when associating with other autonomous algorithms. We then report on the results of a user study in which these algorithms were paired with people in two different Interaction Games. Our results show that while the generalized algorithms demonstrate many of the same properties in Interaction Games as they do in Repeated Games, the complexity of Interaction Games appear to alter the kinds of behaviors that are successful, particularly in environments in which communication between players is not possible.

Game theory can work as a coordination mechanism in multi-agent robotic systems by representing each robot as a player in a game. In ideal scenarios, game theory algorithms guarantee convergence to optimal configurations and have been widely studied for many applications. However, most of the studies focus on theoretical analysis and lack the details of complete demonstrations. In this regard, we implemented a real-time multi-robot system in order to investigate how game-theoretic methods perform in non-idealized settings. An aerial coverage problem was modeled as a potential game, where each aerial vehicle is an independent decision-making player. These players take actions under limited communication, and each is equipped with onboard vision capabilities. Three game-theoretic methods have been modified and implemented to solve this problem. All computations are performed using onboard devices, independent of any ground entity. The performance of the system is analyzed and compared with different tests and configurations

The advances in the technology and the emergence of low complexity intelligent devices result in the evolution of the Internet-of-Things (IoT). In most IoT application scenarios, billions of things are interconnected together using standard communication protocols to provide services for different applications in the healthcare industry, smart cities, transportation, and food supply chain. Despite their advantage of connecting things anywhere, anytime, and anyplace, IoT presents many challenges due to the heterogeneity, density, the power constraints of things, and the dynamic nature of the network that things might connect and disconnect at any time. All of these increase the communication delay and the generated data, and it is thereby necessary to develop resource management solutions for the applications in IoT. One of the most important resources is the wireless channel, which is a shared resource; thus, it is necessary for the nodes to have methods that schedule channel access. This thesis considers the problem of distributed sensing and channel access in the context of IoT systems, where a set of selfish nodes competes for transmission opportunities. In the channel access part, a memory-one channel access game is proposed to reduce the collision rate, to enhance the cooperation among the nodes, and to maximize their payoffs by optimizing their channel access probabilities, based on the channel state in the previous time step. To overcome the communication cost overhead in the network and to solve the problem efficiently, the nodes use distributed learning algorithms. Next, the problem is extended to include energy constraints on the transmission decisions of the nodes, where each one of them has a battery of finite capacity, which is replenished by an energy-harvesting process. This constrained problem is solved using energy-aware channel access games under different scenarios of perfect and imperfect information. In the distributed sensing part, a traffic-monitoring system, integrated into a WSN, is proposed as a potential application to implement the channel access solution. This system maximizes the privacy of the sensed traffic by using low-cost and low-power sensor devices that integrate passive infrared sensors (PIR) and ultrasonic range finders. To estimate the parameters required to solve the real-time monitoring problem (vehicle detection, classification, and speed estimation), the measurements of these sensors are analyzed using a set of optimized machine-learning algorithms. The selection of these algorithms is due to the continuous variation of the sensed environment over time, the lack of the system state dynamic models, and the limitation in the resources.

Cette thèse présente de nouvelles techniques pour les Constraint Games.La manière de résoudre un Constraint Game est repensée en terme de propagation de contraintes.Les préférences des joueurs sont maintenant considérées comme des contraintes globales permettant une intégration transparente dans les solveurs de contraintes ainsi que d'améliorer l'efficacité du framework.Notre nouveau solveur ConGA est diffusé en open source.Celui-ci est plus rapide que les travaux connexes et est capable de trouver tous les équilibres de Nash, et cela même dans des jeux avec 200 joueurs voir 2000 pour certains jeux graphiques.Grâce à cette perspective, le framework a pu être utilisé pour résoudre un problème de routage dans le domaine des télécommunications. Les aspects centralisé et décentralisé ont été étudiés.La comparaison de ces derniers est très importante pour évaluer la qualité de service dans les applications multi-utilisateurs. L'évaluation de cette dernière peut être très coûteuse, c'est pourquoi nous proposons plusieurs techniques permettant d'améliorer la résolution de ce problème et ainsi d'améliorer la résolution du problème. / This thesis revisits the Constraint games framework by rethinking their solving technique in terms of constraint propagation.Players preferences are considered as global constraints making transparently the integration in constraints solvers.It yields not only a more elegant but also a more efficient framework.We release our new solver ConGA in open source.Our new complete solver is faster than previous state-of-the-art and is able to find all pure Nash equilibrium for some problems with 200 players or even with 2000 players in graphical games.This new perspective enables us to tackle real-worlds Telecommunication problems.This problem is solved with a centralized perspective and a decentralized one.The comparison of the two last approaches is really important to evaluate the quality of service in multi-users application, but computationally consuming.That is why, we propose new techniques in order to improve the resolution process.

Melioration learning is an empirically well-grounded model of reinforcement learning. By means of computer simulations, this paper derives predictions for several repeatedly played two-person games from this model. The results indicate a likely convergence to a pure Nash equilibrium of the game. If no pure equilibrium exists, the relative frequencies of choice may approach the predictions of the mixed Nash equilibrium. Yet in some games, no stable state is reached.