Дисертації з теми "Adversarial games"
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Laviers, Kennard R. "Exploiting opponent modeling for learning in multi-agent adversarial games." Doctoral diss., University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4968.
Повний текст джерелаID: 030423259; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (Ph.D.)--University of Central Florida, 2011.; Includes bibliographical references (p. 123-129).
Ph.D.
Doctorate
Electrical Engineering and Computer Science
Engineering and Computer Science
Baker, Roderick J. S. "Bayesian opponent modeling in adversarial game environments." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/5205.
Повний текст джерелаEngineering and Physical Sciences Research Council (EPSRC)
Baker, Roderick James Samuel. "Bayesian opponent modeling in adversarial game environments." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/5205.
Повний текст джерелаBrückner, Michael. "Prediction games : machine learning in the presence of an adversary." Phd thesis, Universität Potsdam, 2012. http://opus.kobv.de/ubp/volltexte/2012/6037/.
Повний текст джерелаEine der Aufgabenstellungen des Maschinellen Lernens ist die Konstruktion von Vorhersagemodellen basierend auf gegebenen Trainingsdaten. Ein solches Modell beschreibt den Zusammenhang zwischen einem Eingabedatum, wie beispielsweise einer E-Mail, und einer Zielgröße; zum Beispiel, ob die E-Mail durch den Empfänger als erwünscht oder unerwünscht empfunden wird. Dabei ist entscheidend, dass ein gelerntes Vorhersagemodell auch die Zielgrößen zuvor unbeobachteter Testdaten korrekt vorhersagt. Die Mehrzahl existierender Lernverfahren wurde unter der Annahme entwickelt, dass Trainings- und Testdaten derselben Wahrscheinlichkeitsverteilung unterliegen. Insbesondere in Fällen in welchen zukünftige Daten von der Wahl des Vorhersagemodells abhängen, ist diese Annahme jedoch verletzt. Ein Beispiel hierfür ist das automatische Filtern von Spam-E-Mails durch E-Mail-Anbieter. Diese konstruieren Spam-Filter basierend auf zuvor empfangenen E-Mails. Die Spam-Sender verändern daraufhin den Inhalt und die Gestaltung der zukünftigen Spam-E-Mails mit dem Ziel, dass diese durch die Filter möglichst nicht erkannt werden. Bisherige Arbeiten zu diesem Thema beschränken sich auf das Lernen robuster Vorhersagemodelle welche unempfindlich gegenüber geringen Veränderungen des datengenerierenden Prozesses sind. Die Modelle werden dabei unter der Worst-Case-Annahme konstruiert, dass diese Veränderungen einen maximal negativen Effekt auf die Vorhersagequalität des Modells haben. Diese Modellierung beschreibt die tatsächliche Wechselwirkung zwischen der Modellbildung und der Generierung zukünftiger Daten nur ungenügend. Aus diesem Grund führen wir in dieser Arbeit das Konzept der Prädiktionsspiele ein. Die Modellbildung wird dabei als mathematisches Spiel zwischen einer lernenden und einer datengenerierenden Instanz beschrieben. Die spieltheoretische Modellierung ermöglicht es uns, die Interaktion der beiden Parteien exakt zu beschreiben. Dies umfasst die jeweils verfolgten Ziele, ihre Handlungsmöglichkeiten, ihr Wissen übereinander und die zeitliche Reihenfolge, in der sie agieren. Insbesondere die Reihenfolge der Spielzüge hat einen entscheidenden Einfluss auf die spieltheoretisch optimale Lösung. Wir betrachten zunächst den Fall gleichzeitig agierender Spieler, in welchem sowohl der Lerner als auch der Datengenerierer keine Kenntnis über die Aktion des jeweils anderen Spielers haben. Wir leiten hinreichende Bedingungen her, unter welchen dieses Spiel eine Lösung in Form eines eindeutigen Nash-Gleichgewichts besitzt. Im Anschluss diskutieren wir zwei verschiedene Verfahren zur effizienten Berechnung dieses Gleichgewichts. Als zweites betrachten wir den Fall eines Stackelberg-Duopols. In diesem Prädiktionsspiel wählt der Lerner zunächst das Vorhersagemodell, woraufhin der Datengenerierer in voller Kenntnis des Modells reagiert. Wir leiten ein relaxiertes Optimierungsproblem zur Bestimmung des Stackelberg-Gleichgewichts her und stellen ein mögliches Lösungsverfahren vor. Darüber hinaus diskutieren wir, inwieweit das Stackelberg-Modell bestehende robuste Lernverfahren verallgemeinert. Abschließend untersuchen wir einen Lerner, der auf die Aktion des Datengenerierers, d.h. der Wahl der Testdaten, reagiert. In diesem Fall sind die Testdaten dem Lerner zum Zeitpunkt der Modellbildung bekannt und können in den Lernprozess einfließen. Allerdings unterliegen die Trainings- und Testdaten nicht notwendigerweise der gleichen Verteilung. Wir leiten daher ein neues integriertes sowie ein zweistufiges Lernverfahren her, welche diese Verteilungsverschiebung bei der Modellbildung berücksichtigen. In mehreren Fallstudien zur Klassifikation von Spam-E-Mails untersuchen wir alle hergeleiteten, sowie existierende Verfahren empirisch. Wir zeigen, dass die hergeleiteten spieltheoretisch-motivierten Lernverfahren in Summe signifikant bessere Spam-Filter erzeugen als alle betrachteten Referenzverfahren.
Eriksson, Lundström Jenny S. Z. "On the Formal Modeling of Games of Language and Adversarial Argumentation : A Logic-Based Artificial Intelligence Approach." Doctoral thesis, Uppsala universitet, Institutionen för informationsvetenskap, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-9538.
Повний текст джерелаEriksson, Lundström Jenny. "On the formal modeling of games of language and adversarial argumentation : a logic-based artificial intelligence approach /." Uppsala : Uppsala universitet, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-9538.
Повний текст джерелаReimann, Johan Michael. "Using Multiplayer Differential Game Theory to Derive Efficient Pursuit-Evasion Strategies for Unmanned Aerial Vehicles." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/16151.
Повний текст джерелаSista, Subrahmanya Srivathsava. "Adversarial Game Playing Using Monte Carlo Tree Search." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479820656701076.
Повний текст джерелаPaget, Bryan. "An Introduction to Generative Adversarial Networks." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39603.
Повний текст джерелаPersson, Louise. "To Kill or Not to Kill : The Moral and Dramatic Potential of Expendable Characters in Role-playing Video Game Narratives." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-12347.
Повний текст джерелаNämerforslund, Tim. "Machine Learning Adversaries in Video Games : Using reinforcement learning in the Unity Engine to create compelling enemy characters." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42746.
Повний текст джерелаAs video games become more complex and more immersive, not just graphically or as an artform, but also technically, it can be expected that games behave on a deeper level to challenge and immerse the player further. Today’s gamers have gotten used to pattern based enemies, moving between preprogrammed states with predictable patterns, which lends itself to a certain kind of gameplay where the goal is to figure out how to beat said pattern. But what if there could be more in terms of challenging the player on an interactive level? What if the enemies could learn and adapt, trying to outsmart the player just as much as the player tries to outsmart the enemies. This is where the field of machine learning enters the stage and opens up for an entirely new type of non-player character in videogames. An enemy who uses a trained machine learning model to play against the player, who can adapt and become better as more people play the game. This study aims to look at early steps to implement machine learning in video games, in this case in the Unity engine, and look at the players perception of said enemies compared to normal state-driven enemies. Via testing voluntary players by letting them play against two kinds of enemies, data is gathered to compare the average performance of the players, after which players answer a questionnaire. These answers are analysed to give an indication of preference in type of enemy. Overall the small scale of the game and simplicity of the enemies gives clear answers but also limits the potential complexity of the enemies and thus the players enjoyment. Though this also enables us to discern a perceived difference in the players experience, where a preference for machine learning controlled enemies is noticeable, as they behave less predictable with more varied behaviour.
Soh, Boon Kee. "Validation of the recognition-primed decision model and the roles of common-sense strategies in an adversarial environment." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/26173.
Повний текст джерелаPh. D.
El, Chamie Mahmoud. "Optimisation, contrôle et théorie des jeux dans les protocoles de consensus." Thesis, Nice, 2014. http://www.theses.fr/2014NICE4094/document.
Повний текст джерелаConsensus protocols have gained a lot of interest in the recent years. In this thesis, we study optimization, control, and game theoretical problems arising in consensus protocols. First, we study optimization techniques for weight selection problems to increase the speed of convergence of discrete-time consensus protocols on networks. We propose to select the weights by applying an approximation algorithm: minimizing the Schatten p-norm of the weight matrix. We characterize the approximation error and we show that the proposed algorithm has the advantage that it can be solved in a totally distributed way. Then we propose a game theoretical framework for an adversary that can add noise to the weights used by averaging protocols to drive the system away from consensus. We give the optimal strategies for the game players (the adversary and the network designer) and we show that a saddle-point equilibrium exists in mixed strategies. We also analyze the performance of distributed averaging algorithms where the information exchanged between neighboring agents is subject to deterministic uniform quantization (e.g., when real values sent by nodes to their neighbors are truncated). Consensus algorithms require that nodes exchange messages persistently to reach asymptotically consensus. We propose a distributed algorithm that reduces the communication overhead while still guaranteeing convergence to consensus. Finally, we propose a score metric that evaluates the quality of clusters such that the faster the random walk mixes in the cluster and the slower it escapes, the higher is the score. A local clustering algorithm based on this metric is proposed
King, Brian D. (Brian David). "Adversarial planning by strategy switching in a real-time strategy game." Thesis, 2012. http://hdl.handle.net/1957/30344.
Повний текст джерелаGraduation date: 2013
"The What, When, and How of Strategic Movement in Adversarial Settings: A Syncretic View of AI and Security." Doctoral diss., 2020. http://hdl.handle.net/2286/R.I.62910.
Повний текст джерелаDissertation/Thesis
Doctoral Dissertation Computer Science 2020
Gidel, Gauthier. "Multi-player games in the era of machine learning." Thesis, 2020. http://hdl.handle.net/1866/24800.
Повний текст джерелаAmong all the historical board games played by humans, the game of go was considered one of the most difficult to master by a computer program [Van Den Heriket al., 2002]; Until it was not [Silver et al., 2016]. This odds-breaking break-through [Müller, 2002, Van Den Herik et al., 2002] came from a sophisticated combination of Monte Carlo tree search and machine learning techniques to evaluate positions, shedding light upon the high potential of machine learning to solve games. Adversarial training, a special case of multiobjective optimization, is an increasingly useful tool in machine learning. For example, two-player zero-sum games are important for generative modeling (GANs) [Goodfellow et al., 2014] and mastering games like Go or Poker via self-play [Silver et al., 2017, Brown and Sandholm,2017]. A classic result in Game Theory states that convex-concave games always have an equilibrium [Neumann, 1928]. Surprisingly, machine learning practitioners successfully train a single pair of neural networks whose objective is a nonconvex-nonconcave minimax problem while for such a payoff function, the existence of a Nash equilibrium is not guaranteed in general. This work is an attempt to put learning in games on a firm theoretical foundation. The first contribution explores minimax theorems for a particular class of nonconvex-nonconcave games that encompasses generative adversarial networks. The proposed result is an approximate minimax theorem for two-player zero-sum games played with neural networks, including WGAN, StarCrat II, and Blotto game. Our findings rely on the fact that despite being nonconcave-nonconvex with respect to the neural networks parameters, the payoff of these games are concave-convex with respect to the actual functions (or distributions) parametrized by these neural networks. The second and third contributions study the optimization of minimax problems, and more generally, variational inequalities in the context of machine learning. While the standard gradient descent-ascent method fails to converge to the Nash equilibrium of simple convex-concave games, there exist ways to use gradients to obtain methods that converge. We investigate several techniques such as extrapolation, averaging and negative momentum. We explore these techniques experimentally by proposing a state-of-the-art (at the time of publication) optimizer for GANs called ExtraAdam. We also prove new convergence results for Extrapolation from the past, originally proposed by Popov [1980], as well as for gradient method with negative momentum. The fourth contribution provides an empirical study of the practical landscape of GANs. In the second and third contributions, we diagnose that the gradient method breaks when the game’s vector field is highly rotational. However, such a situation may describe a worst-case that does not occur in practice. We provide new visualization tools in order to exhibit rotations in practical GAN landscapes. In this contribution, we show empirically that the training of GANs exhibits significant rotations around Local Stable Stationary Points (LSSP), and we provide empirical evidence that GAN training converges to a stable stationary point, which is a saddle point for the generator loss, not a minimum, while still achieving excellent performance.
WANG, CHEN-HAN, and 王振翰. "Generation of Music Game Beatmap via Generative Adversarial Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/rh6vs9.
Повний текст джерела國立中正大學
資訊工程研究所
106
Music games are very popular now, but designing beatmaps usually takes too much time. In addition, there is some limitation of existing methods to generate beatmaps. In this thesis, beatmap generation method is proposed based on Generative Adversarial Networks (GANs). Audio is firstly separated into the vocal and instrument parts to make this method close to beatmap design philosophy of designers. Our model consists of Conditional Generative Adversarial Nets (CGANs) and Improved Wasserstein GAN (WGAN-GP) for considering audio information and fast convergency of model training. Our results are compared with different methods. Besides, we conduct a subjective evaluation of our results and the real beatmaps. Our results are very competitive to the real beatmaps which means our results are close to the real beatmaps.
"Data-Driven and Game-Theoretic Approaches for Privacy." Doctoral diss., 2018. http://hdl.handle.net/2286/R.I.50592.
Повний текст джерелаDissertation/Thesis
Doctoral Dissertation Electrical Engineering 2018
Brehovský, Martin. "Akcelerace adversariálních algoritmů s využití grafického procesoru." Master's thesis, 2011. http://www.nusl.cz/ntk/nusl-295937.
Повний текст джерела