Relevant bibliographies by topics / Adversarial games

Academic literature on the topic 'Adversarial games'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Adversarial games"

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Alon, Noga, Yuval Emek, Michal Feldman, and Moshe Tennenholtz. "Adversarial Leakage in Games." SIAM Journal on Discrete Mathematics 27, no. 1 (January 2013): 363–85. http://dx.doi.org/10.1137/110858021.

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Rosendal, Christian. "Determinacy of adversarial Gowers games." Fundamenta Mathematicae 227, no. 2 (2014): 163–78. http://dx.doi.org/10.4064/fm227-2-3.

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Chorppath, Anil Kumar, Tansu Alpcan, and Holger Boche. "Adversarial Behavior in Network Games." Dynamic Games and Applications 5, no. 1 (August 23, 2014): 26–64. http://dx.doi.org/10.1007/s13235-014-0120-4.

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Banks, David, Francesca Petralia, and Shouqiang Wang. "Adversarial risk analysis: Borel games." Applied Stochastic Models in Business and Industry 27, no. 2 (March 2011): 72–86. http://dx.doi.org/10.1002/asmb.890.

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Wang, Jiali, Xin Jin, and Yang Tang. "Optimal strategy analysis for adversarial differential games." Electronic Research Archive 30, no. 10 (2022): 3692–710. http://dx.doi.org/10.3934/era.2022189.

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<abstract><p>Optimal decision-making and winning-regions analysis in adversarial differential games are challenging theoretical problems because of the complex interactions between players. To solve these problems, we present an organized review for pursuit-evasion games, reach-avoid games and capture-the-flag games; we also outline recent developments in three types of games. First, we summarize recent results for pursuit-evasion games and classify them according to different numbers of players. As a special kind of pursuit-evasion games, target-attacker-defender games with an active target are analyzed from the perspectives of different speed ratios for players. Second, the related works for reach-avoid games and capture-the-flag games are compared in terms of analytical methods and geometric methods, respectively. These methods have different effects on the barriers and optimal strategy analysis between players. Future directions for the pursuit-evasion games, reach-avoid games, capture-the-flag games and their applications are discussed in the end.</p></abstract>
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Yao, Yuan, Haoxi Zhong, Zhengyan Zhang, Xu Han, Xiaozhi Wang, Kai Zhang, Chaojun Xiao, Guoyang Zeng, Zhiyuan Liu, and Maosong Sun. "Adversarial Language Games for Advanced Natural Language Intelligence." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (May 18, 2021): 14248–56. http://dx.doi.org/10.1609/aaai.v35i16.17676.

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We study the problem of adversarial language games, in which multiple agents with conflicting goals compete with each other via natural language interactions. While adversarial language games are ubiquitous in human activities, little attention has been devoted to this field in natural language processing. In this work, we propose a challenging adversarial language game called Adversarial Taboo as an example, in which an attacker and a defender compete around a target word. The attacker is tasked with inducing the defender to utter the target word invisible to the defender, while the defender is tasked with detecting the target word before being induced by the attacker. In Adversarial Taboo, a successful attacker and defender need to hide or infer the intention, and induce or defend during conversations. This requires several advanced language abilities, such as adversarial pragmatic reasoning and goal-oriented language interactions in open domain, which will facilitate many downstream NLP tasks. To instantiate the game, we create a game environment and a competition platform. Comprehensive experiments on several baseline attack and defense strategies show promising and interesting results, based on which we discuss some directions for future research.
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Garnaev, Andrey, Melike Baykal-Gursoy, and H. Vincent Poor. "Security Games With Unknown Adversarial Strategies." IEEE Transactions on Cybernetics 46, no. 10 (October 2016): 2291–99. http://dx.doi.org/10.1109/tcyb.2015.2475243.

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Wu, Yuhang, Sunpreet S. Arora, Yanhong Wu, and Hao Yang. "Beating Attackers At Their Own Games: Adversarial Example Detection Using Adversarial Gradient Directions." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (May 18, 2021): 2969–77. http://dx.doi.org/10.1609/aaai.v35i4.16404.

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Adversarial examples are input examples that are specifically crafted to deceive machine learning classifiers. State-of-the-art adversarial example detection methods characterize an input example as adversarial either by quantifying the magnitude of feature variations under multiple perturbations or by measuring its distance from estimated benign example distribution. Instead of using such metrics, the proposed method is based on the observation that the directions of adversarial gradients when crafting (new) adversarial examples play a key role in characterizing the adversarial space. Compared to detection methods that use multiple perturbations, the proposed method is efficient as it only applies a single random perturbation on the input example. Experiments conducted on two different databases, CIFAR-10 and ImageNet, show that the proposed detection method achieves, respectively, 97.9% and 98.6% AUC-ROC (on average) on five different adversarial attacks, and outperforms multiple state-of-the-art detection methods. Results demonstrate the effectiveness of using adversarial gradient directions for adversarial example detection.
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Wampler, Kevin, Erik Andersen, Evan Herbst, Yongjoon Lee, and Zoran Popović. "Character animation in two-player adversarial games." ACM Transactions on Graphics 29, no. 3 (June 2010): 1–13. http://dx.doi.org/10.1145/1805964.1805970.

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Rota Bulo, Samuel, Battista Biggio, Ignazio Pillai, Marcello Pelillo, and Fabio Roli. "Randomized Prediction Games for Adversarial Machine Learning." IEEE Transactions on Neural Networks and Learning Systems 28, no. 11 (November 2017): 2466–78. http://dx.doi.org/10.1109/tnnls.2016.2593488.

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Dissertations / Theses on the topic "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.

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An issue with learning effective policies in multi-agent adversarial games is that the size of the search space can be prohibitively large when the actions of both teammates and opponents are considered simultaneously. Opponent modeling, predicting an opponent's actions in advance of execution, is one approach for selecting actions in adversarial settings, but it is often performed in an ad hoc way. In this dissertation, we introduce several methods for using opponent modeling, in the form of predictions about the players' physical movements, to learn team policies. To explore the problem of decision-making in multi-agent adversarial scenarios, we use our approach for both offline play generation and real-time team response in the Rush 2008 American football simulator. Simultaneously predicting the movement trajectories, future reward, and play strategies of multiple players in real-time is a daunting task but we illustrate how it is possible to divide and conquer this problem with an assortment of data-driven models. By leveraging spatio-temporal traces of player movements, we learn discriminative models of defensive play for opponent modeling. With the reward information from previous play matchups, we use a modified version of UCT (Upper Conference Bounds applied to Trees) to create new offensive plays and to learn play repairs to counter predicted opponent actions. In team games, players must coordinate effectively to accomplish tasks while foiling their opponents either in a preplanned or emergent manner. An effective team policy must generate the necessary coordination, yet considering all possibilities for creating coordinating subgroups is computationally infeasible. Automatically identifying and preserving the coordination between key subgroups of teammates can make search more productive by pruning policies that disrupt these relationships.; We demonstrate that combining opponent modeling with automatic subgroup identification can be used to create team policies with a higher average yardage than either the baseline game or domain-specific heuristics.
ID: 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
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Baker, Roderick J. S. "Bayesian opponent modeling in adversarial game environments." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/5205.

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This thesis investigates the use of Bayesian analysis upon an opponent¿s behaviour in order to determine the desired goals or strategy used by a given adversary. A terrain analysis approach utilising the A* algorithm is investigated, where a probability distribution between discrete behaviours of an opponent relative to a set of possible goals is generated. The Bayesian analysis of agent behaviour accurately determines the intended goal of an opponent agent, even when the opponent¿s actions are altered randomly. The environment of Poker is introduced and abstracted for ease of analysis. Bayes¿ theorem is used to generate an effective opponent model, categorizing behaviour according to its similarity with known styles of opponent. The accuracy of Bayes¿ rule yields a notable improvement in the performance of an agent once an opponent¿s style is understood. A hybrid of the Bayesian style predictor and a neuroevolutionary approach is shown to lead to effective dynamic play, in comparison to agents that do not use an opponent model. The use of recurrence in evolved networks is also shown to improve the performance and generalizability of an agent in a multiplayer environment. These strategies are then employed in the full-scale environment of Texas Hold¿em, where a betting round-based approach proves useful in determining and counteracting an opponent¿s play. It is shown that the use of opponent models, with the adaptive benefits of neuroevolution aid the performance of an agent, even when the behaviour of an opponent does not necessarily fit within the strict definitions of opponent ¿style¿.
Engineering and Physical Sciences Research Council (EPSRC)
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Baker, Roderick James Samuel. "Bayesian opponent modeling in adversarial game environments." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/5205.

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This thesis investigates the use of Bayesian analysis upon an opponent's behaviour in order to determine the desired goals or strategy used by a given adversary. A terrain analysis approach utilising the A* algorithm is investigated, where a probability distribution between discrete behaviours of an opponent relative to a set of possible goals is generated. The Bayesian analysis of agent behaviour accurately determines the intended goal of an opponent agent, even when the opponent's actions are altered randomly. The environment of Poker is introduced and abstracted for ease of analysis. Bayes' theorem is used to generate an effective opponent model, categorizing behaviour according to its similarity with known styles of opponent. The accuracy of Bayes' rule yields a notable improvement in the performance of an agent once an opponent's style is understood. A hybrid of the Bayesian style predictor and a neuroevolutionary approach is shown to lead to effective dynamic play, in comparison to agents that do not use an opponent model. The use of recurrence in evolved networks is also shown to improve the performance and generalizability of an agent in a multiplayer environment. These strategies are then employed in the full-scale environment of Texas Hold'em, where a betting round-based approach proves useful in determining and counteracting an opponent's play. It is shown that the use of opponent models, with the adaptive benefits of neuroevolution aid the performance of an agent, even when the behaviour of an opponent does not necessarily fit within the strict definitions of opponent 'style'.
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Brückner, Michael. "Prediction games : machine learning in the presence of an adversary." Phd thesis, Universität Potsdam, 2012. http://opus.kobv.de/ubp/volltexte/2012/6037/.

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In many applications one is faced with the problem of inferring some functional relation between input and output variables from given data. Consider, for instance, the task of email spam filtering where one seeks to find a model which automatically assigns new, previously unseen emails to class spam or non-spam. Building such a predictive model based on observed training inputs (e.g., emails) with corresponding outputs (e.g., spam labels) is a major goal of machine learning. Many learning methods assume that these training data are governed by the same distribution as the test data which the predictive model will be exposed to at application time. That assumption is violated when the test data are generated in response to the presence of a predictive model. This becomes apparent, for instance, in the above example of email spam filtering. Here, email service providers employ spam filters and spam senders engineer campaign templates such as to achieve a high rate of successful deliveries despite any filters. Most of the existing work casts such situations as learning robust models which are unsusceptible against small changes of the data generation process. The models are constructed under the worst-case assumption that these changes are performed such to produce the highest possible adverse effect on the performance of the predictive model. However, this approach is not capable to realistically model the true dependency between the model-building process and the process of generating future data. We therefore establish the concept of prediction games: We model the interaction between a learner, who builds the predictive model, and a data generator, who controls the process of data generation, as an one-shot game. The game-theoretic framework enables us to explicitly model the players' interests, their possible actions, their level of knowledge about each other, and the order at which they decide for an action. We model the players' interests as minimizing their own cost function which both depend on both players' actions. The learner's action is to choose the model parameters and the data generator's action is to perturbate the training data which reflects the modification of the data generation process with respect to the past data. We extensively study three instances of prediction games which differ regarding the order in which the players decide for their action. We first assume that both player choose their actions simultaneously, that is, without the knowledge of their opponent's decision. We identify conditions under which this Nash prediction game has a meaningful solution, that is, a unique Nash equilibrium, and derive algorithms that find the equilibrial prediction model. As a second case, we consider a data generator who is potentially fully informed about the move of the learner. This setting establishes a Stackelberg competition. We derive a relaxed optimization criterion to determine the solution of this game and show that this Stackelberg prediction game generalizes existing prediction models. Finally, we study the setting where the learner observes the data generator's action, that is, the (unlabeled) test data, before building the predictive model. As the test data and the training data may be governed by differing probability distributions, this scenario reduces to learning under covariate shift. We derive a new integrated as well as a two-stage method to account for this data set shift. In case studies on email spam filtering we empirically explore properties of all derived models as well as several existing baseline methods. We show that spam filters resulting from the Nash prediction game as well as the Stackelberg prediction game in the majority of cases outperform other existing baseline methods.
Eine 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.
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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.

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Argumentation is a highly dynamical and dialectical process drawing on human cognition. Successful argumentation is ubiquitous to human interaction. Comprehensive formal modeling and analysis of argumentation presupposes a dynamical approach to the following phenomena: the deductive logic notion, the dialectical notion and the cognitive notion of justified belief. For each step of an argumentation these phenomena form networks of rules which determine the propositions to be allowed to make sense as admissible, acceptable, and accepted. We present a formal logic framework for a computational account of formal modeling and systematical analysis of the dynamical, exhaustive and dialectical aspects of adversarial argumentation and dispute. Our approach addresses the mechanisms of admissibility, acceptability and acceptance of arguments in adversarial argumentation by use of metalogic representation and Artificial Intelligence-techniques for dynamical problem solving by exhaustive search. We elaborate on a common framework of board games and argumentation games for pursuing the alternatives facing the adversaries in the argumentation process conceived as a game. The analogy to chess is beneficial as it incorporates strategic and tactical operations just as argumentation. Drawing on an analogy to board games like chess, the state space representation, well researched in Artificial Intelligence, allows for a treatment of all possible arguments as paths in a directed state space graph. It will render a game leading to the most wins and fewest losses, identifying the most effective game strategy. As an alternate visualization, the traversal of the state space graph unravels and collates knowledge about the given situation/case under dispute. Including the private knowledge of the two parties, the traversal results in an increased knowledge of the case and the perspectives and arguments of the participants. As we adopt metalogic as formal basis, arguments used in the argumentation, expressed in a non-monotonic defeasible logic, are encoded as terms in the logical argumentation analysis system. The advantage of a logical formalization of argumentation is that it provides a symbolic knowledge representation with a formally well-formed semantics, making the represented knowledge as well as the behavior of knowledge representation systems reasoning comprehensible. Computational logic as represented in Horn Clauses allows for expression of substantive propositions in a logical structure. The non-monotonic nature of defeasible logic stresses the representational issues, i.e. what is possible to capture in non-monotonic reasoning, while from the (meta)logic program, the sound computation on what it is possible to compute, and how to regard the semantics of this computation, are established.
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Eriksson, 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.

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Reimann, 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.

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In recent years, Unmanned Aerial Vehicles (UAVs) have been used extensively in military conflict situations to execute intelligence, surveillance and reconnaissance missions. However, most of the current UAV platforms have limited collaborative capabilities, and consequently they must be controlled individually by operators on the ground. The purpose of the research presented in this thesis is to derive algorithms that can enable multiple UAVs to reason about the movements of multiple ground targets and autonomously coordinate their efforts in real-time to ensure that the targets do not escape. By improving the autonomy of multivehicle systems, the workload placed on the command and control operators is reduced significantly. To derive effective adversarial control algorithms, the adversarial scenario is modeled as a multiplayer differential game. However, due to the inherent computational complexity of multiplayer differential games, three less computationally demanding differential pursuit-evasion game-based algorithms are presented. The purpose of the algorithms is to quickly derive interception strategies for a team of autonomous vehicles. The algorithms are applicable to scenarios with different base assumptions, that is, the three algorithms are meant to complement one another by addressing different types of adversarial problems.
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Sista, Subrahmanya Srivathsava. "Adversarial Game Playing Using Monte Carlo Tree Search." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479820656701076.

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Paget, Bryan. "An Introduction to Generative Adversarial Networks." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39603.

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Persson, 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.

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Killing in role-playing video games is often a prominent feature. Most of the times, the characters killed are nameless criminals or minions of the true antagonist and if the game wants the player to kill, the player will most probably kill. This research was conducted to see how a dynamic narrative could affect a player’s choice of whether or not to kill expendable adversaries when a choice was provided. Participants played an interactive narrative in two different versions, followed by interviews, to see how narrative consequences and mechanisms for moral disengagement affected the players’ choices. The results showed that the choice of whether or not to kill could be affected if the narrative is dynamic and the non-playable characters reflect upon the choices made. Future studies should be conducted to see how graphics and sound affect the choices, and to see if it might be the mere choice in itself that affects the players the most.
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Books on the topic "Adversarial games"

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Horowitz, Andrew W. Beyond indifferent players: On the existence of prisoners dilemmas in games with amicable and adversarial preferences. Antwerp, Belgium: Institute of Development Policy and Management, University of Antwerp, 2005.

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Miller, Steve, Michael Mikaelian, Owen K. C. Stephens, Eric Cagle, Michelle Lyons, and Wil Upchurch. Ultimate Adversaries. Renton, Washington, United States of America: Wizards of the Coast, 2004.

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author, Rios Jesus, and Ríos Insua, David, 1964- author, eds. Adversarial risk analysis. Boca Raton: CRC Press, Taylor & Francis Group, 2016.

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Axelrod, Alan. Risk: Adversaries and allies : mastering strategic relationships. New York: Sterling Pub., 2009.

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Jajodia, Sushil. Moving Target Defense II: Application of Game Theory and Adversarial Modeling. New York, NY: Springer New York, 2013.

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Adversarial Reasoning. London: Taylor and Francis, 2006.

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Banks, David L., David Rios Insua, and Jesus M. Rios Aliaga. Adversarial Risk Analysis. Taylor & Francis Group, 2015.

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Banks, David L., David Rios Insua, and Jesus M. Rios Aliaga. Adversarial Risk Analysis. Taylor & Francis Group, 2015.

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McEneaney, William M., and Alexander Kott. Adversarial Reasoning: Computational Approaches to Reading the Opponent's Mind. Taylor & Francis Group, 2006.

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(Editor), Alexander Kott, and William M. McEneaney (Editor), eds. Adversarial Reasoning: Computational Approaches to Reading the Opponent's Mind. Chapman & Hall/CRC, 2006.

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Book chapters on the topic "Adversarial games"

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Willmott, Steven, Julian Richardson, Alan Bundy, and John Levine. "An Adversarial Planning Approach to Go." In Computers and Games, 93–112. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48957-6_6.

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Sreevallabh Chivukula, Aneesh, Xinghao Yang, and Wei Liu. "Adversarial Deep Learning with Stackelberg Games." In Communications in Computer and Information Science, 3–12. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36808-1_1.

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Zhang, Yi, and Sanjiv Kapoor. "Budgeted Adversarial Network Resource Utilization Games." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 339–65. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23141-4_25.

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Justesen, Niels, Tobias Mahlmann, and Julian Togelius. "Online Evolution for Multi-action Adversarial Games." In Applications of Evolutionary Computation, 590–603. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31204-0_38.

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Zhou, Yan, and Murat Kantarcioglu. "Modeling Adversarial Learning as Nested Stackelberg Games." In Advances in Knowledge Discovery and Data Mining, 350–62. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31750-2_28.

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Mittalella, Nimisha, Priyanjali Pratap Singh, and Prerna Sharma. "Generative Adversarial Networks Based PCG for Games." In Deep Learning in Gaming and Animations, 137–56. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003231530-8.

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Dhounchak, Ranbir, Veeraruna Kavitha, and Yezekael Hayel. "To Participate or Not in a Coalition in Adversarial Games." In Network Games, Control, and Optimization, 125–44. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10880-9_8.

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Kiumarsi, Bahare, and Tamer Başar. "Distributed Aggregative Games on Graphs in Adversarial Environments." In Lecture Notes in Computer Science, 296–313. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01554-1_17.

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Langley, Alexander, Vikas Dhiman, and Henrik Christensen. "Heterogeneous Multi-robot Adversarial Patrolling Using Polymatrix Games." In Advances in Automation, Mechanical and Design Engineering, 13–27. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09909-0_2.

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Huang, Linan, and Quanyan Zhu. "Dynamic Bayesian Games for Adversarial and Defensive Cyber Deception." In Autonomous Cyber Deception, 75–97. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-02110-8_5.

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Conference papers on the topic "Adversarial games"

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Yang, Pei, Qi Tan, Jieping Ye, Hanghang Tong, and Jingrui He. "Deep Multi-Task Learning with Adversarial-and-Cooperative Nets." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/566.

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In this paper, we propose a deep multi-Task learning model based on Adversarial-and-COoperative nets (TACO). The goal is to use an adversarial-and-cooperative strategy to decouple the task-common and task-specific knowledge, facilitating the fine-grained knowledge sharing among tasks. TACO accommodates multiple game players, i.e., feature extractors, domain discriminator, and tri-classifiers. They play the MinMax games adversarially and cooperatively to distill the task-common and task-specific features, while respecting their discriminative structures. Moreover, it adopts a divide-and-combine strategy to leverage the decoupled multi-view information to further improve the generalization performance of the model. The experimental results show that our proposed method significantly outperforms the state-of-the-art algorithms on the benchmark datasets in both multi-task learning and semi-supervised domain adaptation scenarios.
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Rismiller, Sean C., Jonathan Cagan, and Christopher McComb. "Stochastic Stackelberg Games for Agent-Driven Robust Design." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22153.

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Abstract Products must often endure unpredictable and challenging conditions while fulfilling their intended functions. Game-theoretic methods make it possible for designers to design solutions that are robust against complicated conditions, however, these methods are often specific to the problems they investigate. This work introduces the Game-Augmented Robust Simulated Annealing Teams (GARSAT) framework, a game-theoretic agent-based architecture that generates solutions robust to variation, and models problems with elementary information, making it easily extendable. The platform was used to generate designs under consideration of a multidimensional attack. Designs were produced under various adversarial settings and compared to designs generated without considering adversaries to validate the model. The process successfully created robust designs able to withstand multiple combined conditions, and the effects of the adversarial settings on the designs were explored.
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Ferstl, Ylva, Michael Neff, and Rachel McDonnell. "Multi-objective adversarial gesture generation." In MIG '19: Motion, Interaction and Games. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3359566.3360053.

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Duan, Jiali, Qian Wang, Lerrel Pinto, C. C. Jay Kuo, and Stefanos Nikolaidis. "Robot Learning via Human Adversarial Games." In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019. http://dx.doi.org/10.1109/iros40897.2019.8968306.

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Brückner, Michael, and Tobias Scheffer. "Stackelberg games for adversarial prediction problems." In the 17th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2020408.2020495.

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Skoulakis, I. E., and M. G. Lagoudakis. "Efficient Reinforcement Learning in Adversarial Games." In 2012 IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI 2012). IEEE, 2012. http://dx.doi.org/10.1109/ictai.2012.100.

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Sailer, Frantisek, Michael Buro, and Marc Lanctot. "Adversarial Planning Through Strategy Simulation." In 2007 IEEE Symposium on Computational Intelligence and Games. IEEE, 2007. http://dx.doi.org/10.1109/cig.2007.368082.

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Gisslen, Linus, Andy Eakins, Camilo Gordillo, Joakim Bergdahl, and Konrad Tollmar. "Adversarial Reinforcement Learning for Procedural Content Generation." In 2021 IEEE Conference on Games (CoG). IEEE, 2021. http://dx.doi.org/10.1109/cog52621.2021.9619053.

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Giacomello, Edoardo, Pier Luca Lanzi, and Daniele Loiacono. "DOOM Level Generation Using Generative Adversarial Networks." In 2018 IEEE Games, Entertainment, Media Conference (GEM). IEEE, 2018. http://dx.doi.org/10.1109/gem.2018.8516539.

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Baier, Hendrik, and Peter I. Cowling. "Evolutionary MCTS for Multi-Action Adversarial Games." In 2018 IEEE Conference on Computational Intelligence and Games (CIG). IEEE, 2018. http://dx.doi.org/10.1109/cig.2018.8490403.

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Reports on the topic "Adversarial games"

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Zilberstein, Shlomo. New Algorithms for Collaborative and Adversarial Decision Making in Partially Observable Stochastic Games. Fort Belvoir, VA: Defense Technical Information Center, January 2009. http://dx.doi.org/10.21236/ada495149.

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McBride, Michael, Ryan Kendall, Martin B. Short, and Maria R. D'Orsogna. Crime, Punishment, and Evolution in an Adversarial Game. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada589643.

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