Relevant bibliographies by topics / Artificial Intelligence on games

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Artificial Intelligence on games'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Artificial Intelligence on games"

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Lucas, Simon. "Artificial Intelligence and Games." KI - Künstliche Intelligenz 34, no. 1 (2020): 87–88. http://dx.doi.org/10.1007/s13218-020-00646-x.

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Schaeffer, Jonathan, and H. Jaap van den Herik. "Games, computers, and artificial intelligence." Artificial Intelligence 134, no. 1-2 (2002): 1–7. http://dx.doi.org/10.1016/s0004-3702(01)00165-5.

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El Rhalibi, Abdennour, Kok Wai Wong, and Marc Price. "Artificial Intelligence for Computer Games." International Journal of Computer Games Technology 2009 (2009): 1–3. http://dx.doi.org/10.1155/2009/251652.

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Barash, Guy, Mauricio Castillo-Effen, Niyati Chhaya, et al. "Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence." AI Magazine 40, no. 3 (2019): 67–78. http://dx.doi.org/10.1609/aimag.v40i3.4981.

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The workshop program of the Association for the Advancement of Artificial Intelligence’s 33rd Conference on Artificial Intelligence (AAAI-19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27–28, 2019. There were fifteen workshops in the program: Affective Content Analysis: Modeling Affect-in-Action, Agile Robotics for Industrial Automation Competition, Artificial Intelligence for Cyber Security, Artificial Intelligence Safety, Dialog System Technology Challenge, Engineering Dependable and Secure Machine Learning Systems, Games and Simulations for Artificial Intelligence, Health I
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Rana, Priya, Parthik Bhardwaj, and Jyotsna Singh. "Artificial Intelligence (AI) in Video Games." International Journal of Computer Applications 181, no. 19 (2018): 1–3. http://dx.doi.org/10.5120/ijca2018917818.

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Rodin, E. Y., Y. Lirov, S. Mittnik, B. G. McElhaney, and L. Wilbur. "Artificial intelligence in air combat games." Computers & Mathematics with Applications 13, no. 1-3 (1987): 261–74. http://dx.doi.org/10.1016/0898-1221(87)90109-x.

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Ganguly, Rajjeshwar, Dubba Rithvik Reddy, Revathi Venkataraman, and Sharanya S. "Review on foreground artificial intelligence in games." International Journal of Engineering & Technology 7, no. 2.8 (2018): 453. http://dx.doi.org/10.14419/ijet.v7i2.8.10482.

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Artificial Intelligence (AI) is applied in almost every field existing in today's world and video games prove to be an excellent ground due to its responsive and intelligent behaviour. The games can be put to use model human- level AI, machine learning and scripting behaviour. This work deals with AI used in games to create more complicated and human like behaviour in the non player characters. Unlike most commercial games, games involving AI don’t use the AI in the background rather it is used in the foreground to enhance player experience. An analysis of use of the AI in a number of existing
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Naumov, Pavel, and Yuan Yuan. "Intelligence in Strategic Games." Journal of Artificial Intelligence Research 71 (July 24, 2021): 521–56. http://dx.doi.org/10.1613/jair.1.12883.

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If an agent, or a coalition of agents, has a strategy, knows that she has a strategy, and knows what the strategy is, then she has a know-how strategy. Several modal logics of coalition power for know-how strategies have been studied before.
 The contribution of the article is three-fold. First, it proposes a new class of know-how strategies that depend on the intelligence information about the opponents’ actions. Second, it shows that the coalition power modality for the proposed new class of strategies cannot be expressed through the standard know-how modality. Third, it gives a sound a
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Bátfai, Norbert. "A játékok és a mesterséges intelligencia mint a kultúra jövője – egy kísérlet a szubjektivitás elméletének kialakítására." Információs Társadalom 18, no. 2 (2018): 28. http://dx.doi.org/10.22503/inftars.xviii.2018.2.2.

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A cikk célja a mesterséges intelligencia kutatásokat az emberi önmegismerés szolgálatába állítani. Ehhez egyrészt filozófiai hátteret biztosítani, másrészt a mesterséges intelligencia társadalmi elfogadottságát megalapozni. Tézisünk, hogy az emberi kultúra fenntartásához és fejlesztéséhez a játékokon és a mesterséges intelligencián keresztül vezet az út. E tézis alátámasztásnak támogatására kísérletet teszünk a szubjektivitás elméletének megalapozására.
 
 ---
 
 Games and artificial intelligence as the future of culture: an attempt to develop a theory of subjectivity
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Ontañón, Santiago, Nicolas A. Barriga, Cleyton R. Silva, Rubens O. Moraes, and Levi H. S. Lelis. "The First microRTS Artificial Intelligence Competition." AI Magazine 39, no. 1 (2018): 75–83. http://dx.doi.org/10.1609/aimag.v39i1.2777.

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This article presents the results of the first edition of the microRTS (μRTS) AI competition, which was hosted by the IEEE Computational Intelligence in Games (CIG) 2017 conference. The goal of the competition is to spur research on AI techniques for real-time strategy (RTS) games. In this first edition, the competition received three submissions, focusing on address- ing problems such as balancing long-term and short-term search, the use of machine learning to learn how to play against certain opponents, and finally, dealing with partial observability in RTS games.
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Dissertations / Theses on the topic "Artificial Intelligence on games"

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Zhadan, Anastasiia. "Artificial Intelligence Adaptation in Video Games." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-79131.

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One of the most important features of a (computer) game that makes it memorable is an ability to bring a sense of engagement. This can be achieved in numerous ways, but the most major part is a challenge, often provided by in-game enemies and their ability to adapt towards the human player. However, adaptability is not very common in games. Throughout this thesis work, aspects of the game control systems that can be improved in order to be adaptable were studied. Based on the results gained from the study of the literature related to artificial intelligence in games, a classification of games
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KARLSSON, BORJE FELIPE FERNANDES. "AN ARTIFICIAL INTELLIGENCE MIDDLEWARE FOR DIGITAL GAMES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2005. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7861@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>A aplicação de inteligência artificial (IA) em jogos digitais atualmente se encontra sob uma constante necessidade de melhorias, na tentaiva de atender as crescentes demandas dos jogadores por realismo e credibilidade no comportamento dos personagens do universo do jogo. De modo a facilitar o atendimento destas demandas, técnicas e metodologias de engenharia de software vêm sendo utilizadas no desenvolvimento de jogos. No entanto, o uso destas técnicas e a construção de middlewares na área de IA ainda está longe de gerar
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Edlund, Mattias. "Artificial Intelligence in Games : Faking Human Behavior." Thesis, Uppsala universitet, Institutionen för speldesign, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-258222.

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This paper examines the possibilities of faking human behavior with artificial intelligence in computer games, by using efficient methods that save valuable development time and also creates a more rich experience for the players of a game. The specific implementation of artificial intelligence created and discussed is a neural network controlling a finite-state machine. The objective was to mimic human behavior rather than simulating true intelligence. A 2D shooter game is developed and used for experiments performed with human and artificial intelligence controlled players. The game sessions
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Hedberg, Charlie Forsberg, and Alexander Pedersen. "Artificial Intelligence : Memory-driven decisions in games." Thesis, Blekinge Tekniska Högskola, Institutionen för teknik och estetik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3640.

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Developing AI (Artificial Intelligence) for games can be a hard and challenging task. It is sometimes desired to create behaviors that follow some sort of logical pattern. In order to do this, information must be gathered and processed. This bachelor thesis presents an algorithm that could assist current AI technologies to collect and memorize environmental data. The thesis also covers practical implementation guidelines, established through research and testing.<br>Att utveckla AI (Artificiell Intelligence) i spel kan vara en hård och utmanande uppgift. Ibland är det önskvärt att skapa beteen
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Allis, Louis Victor. "Searching for solutions in games and artificial intelligence." Maastricht : Maastricht : Rijksuniversiteit Limburg ; University Library, Maastricht University [Host], 1994. http://arno.unimaas.nl/show.cgi?fid=6868.

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Saini, Simardeep S. "Mimicking human player strategies in fighting games using game artificial intelligence techniques." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/16380.

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Fighting videogames (also known as fighting games) are ever growing in popularity and accessibility. The isolated console experiences of 20th century gaming has been replaced by online gaming services that allow gamers to play from almost anywhere in the world with one another. This gives rise to competitive gaming on a global scale enabling them to experience fresh play styles and challenges by playing someone new. Fighting games can typically be played either as a single player experience, or against another human player, whether it is via a network or a traditional multiplayer experience. H
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Nilsson, Joakim, and Andreas Jonasson. "Using Artificial Intelligence for Gameplay Testing On Turn-Based Games." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16716.

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Background. Game development is a constantly evolving multi billion dollar in-dustry, and the need for quality products is very high. Testing the games however isa very time consuming and tedious task, often coming down to repeating sequencesuntil a requirement has been met. But what if some parts of it could be automated,handled by an artificial intelligence that can play the game day and night, givingstatistics about the gameplay as well as reports about errors that occurred duringthe session? Objectives. This thesis is done in cooperation with Fall Damage Studio AB, andaims to find and impl
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Wei, Ermo. "Learning to Play Cooperative Games via Reinforcement Learning." Thesis, George Mason University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13420351.

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<p> Being able to accomplish tasks with multiple learners through learning has long been a goal of the multiagent systems and machine learning communities. One of the main approaches people have taken is reinforcement learning, but due to certain conditions and restrictions, applying reinforcement learning in a multiagent setting has not achieved the same level of success when compared to its single agent counterparts. </p><p> This thesis aims to make coordination better for agents in cooperative games by improving on reinforcement learning algorithms in several ways. I begin by examining ce
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Liu, Siming. "Evolving effective micro behaviors for real-time strategy games." Thesis, University of Nevada, Reno, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3707862.

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<p> Real-Time Strategy games have become a new frontier of artificial intelligence research. Advances in real-time strategy game AI, like with chess and checkers before, will significantly advance the state of the art in AI research. This thesis aims to investigate using heuristic search algorithms to generate effective micro behaviors in combat scenarios for real-time strategy games. <i> Macro</i> and <i>micro</i> management are two key aspects of real-time strategy games. While good macro helps a player collect more resources and build more units, good micro helps a player win skirmishes aga
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Stene, Sindre Berg. "Artificial Intelligence Techniques in Real-Time Strategy Games - Architecture and Combat Behavior." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2006. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9497.

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<p>The general purpose of this research is to investigate the possibilities offered for the use of Artificial Intelligence theory and methods in advanced game environments. The real-time strategy (RTS) game genre is investigated in detail, and an architecture and solutions to some common issues are presented. An RTS AI controlled opponent named “KAI” is implemented for the “TA Spring” game engine in order to advance the state of the art in usin AI techniques in games and to gain some insight into the strengths and weaknesses of AI Controlled Player (AI CP) architectures. A goal was to create
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Books on the topic "Artificial Intelligence on games"

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1968-, Funge John David, ed. Artificial intelligence for games. 2nd ed. Elsevier Morgan Kaufmann, 2009.

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Yannakakis, Georgios N., and Julian Togelius. Artificial Intelligence and Games. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63519-4.

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Artificial intelligence and computer games. Century Communications, 1985.

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Calero, Pedro A. González. Artificial Intelligence for Computer Games. Springer Science+Business Media, LLC, 2011.

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González-Calero, Pedro Antonio, and Marco Antonio Gómez-Martín, eds. Artificial Intelligence for Computer Games. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8188-2.

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Baba, Norio. Computational Intelligence in Games. Physica-Verlag HD, 2001.

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name, No. Chips challenging champions: Games, computers and artificial intelligence. Elsevier, 2002.

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Artificial morality: Virtuous robots for virtual games. Routledge, 2002.

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Ahlquist, John. Game development essentials: Game artificial intelligence. Thomson/Delmar Learning, 2008.

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Artificial morality: Virtuous robots for virtual games. Routledge, 1992.

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Book chapters on the topic "Artificial Intelligence on games"

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Satoi, Daiki, and Yuta Mizuno. "Meta Artificial Intelligence and Artificial Intelligence Director." In Encyclopedia of Computer Graphics and Games. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-08234-9_309-1.

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Bouzy, Bruno, Tristan Cazenave, Vincent Corruble, and Olivier Teytaud. "Artificial Intelligence for Games." In A Guided Tour of Artificial Intelligence Research. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-06167-8_11.

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Gravot, Fabien. "Navigation Artificial Intelligence." In Encyclopedia of Computer Graphics and Games. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-08234-9_310-1.

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Yannakakis, Georgios N., and Julian Togelius. "Playing Games." In Artificial Intelligence and Games. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63519-4_3.

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Halpern, Jared. "Artificial Intelligence and Slingshots." In Developing 2D Games with Unity. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3772-4_8.

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Hogg, Chad, Stephen Lee-Urban, Héctor Muñoz-Avila, Bryan Auslander, and Megan Smith. "Game AI for Domination Games." In Artificial Intelligence for Computer Games. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8188-2_4.

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Hildmann, Hanno, and Benjamin Hirsch. "Overview of Artificial Intelligence." In Encyclopedia of Computer Graphics and Games. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-08234-9_228-1.

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Finzi, Alberto, and Thomas Lukasiewicz. "Relational Markov Games." In Logics in Artificial Intelligence. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30227-8_28.

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Hildmann, Hanno. "Computer Games and Artificial Intelligence." In Encyclopedia of Computer Graphics and Games. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-08234-9_234-1.

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DiMarzio, J. F. "Adding Basic Enemy Artificial Intelligence." In Practical Android 4 Games Development. Apress, 2011. http://dx.doi.org/10.1007/978-1-4302-4030-3_7.

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Conference papers on the topic "Artificial Intelligence on games"

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Monteiro, Juarez, Roger Granada, Rafael C. Pinto, and Rodrigo C. Barros. "Beating Bomberman with Artificial Intelligence." In XV Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/eniac.2018.4430.

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Artificial Intelligence (AI) seeks to bring intelligent behavior for machines by using specific techniques. These techniques can be employed in order to solve tasks, such as planning paths or controlling intelligent agents. Some tasks that use AI techniques are not trivially testable, since it can handle a high number of variables depending on their complexity. As digital games can provide a wide range of variables, they become an efficient and economical means for testing artificial intelligence techniques. In this paper, we propose a combination of a behavior tree and a Pathfinding algorithm
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Lecchi, Stefano. "Artificial intelligence in racing games." In 2009 IEEE Symposium on Computational Intelligence and Games (CIG). IEEE, 2009. http://dx.doi.org/10.1109/cig.2009.5286512.

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Simon, Sunil, and Dominik Wojtczak. "Synchronisation Games on Hypergraphs." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/57.

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We study a strategic game model on hypergraphs where players, modelled by nodes, try to coordinate or anti-coordinate their choices within certain groups of players, modelled by hyperedges. We show this model to be a strict generalisation of symmetric additively separable hedonic games to the hypergraph setting and that such games always have a pure Nash equilibrium, which can be computed in pseudo-polynomial time. Moreover, in the pure coordination setting, we show that a strong equilibrium exists and can be computed in polynomial time when the game possesses a certain acyclic structure.
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Harrenstein, Paul, Paolo Turrini, and Michael Wooldridge. "Characterising the Manipulability of Boolean Games." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/150.

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The existence of (Nash) equilibria with undesirable properties is a well-known problem in game theory, which has motivated much research directed at the possibility of mechanisms for modifying games in order to eliminate undesirable equilibria, or induce desirable ones. Taxation schemes are a well-known mechanism for modifying games in this way. In the multi-agent systems community, taxation mechanisms for incentive engineering have been studied in the context of Boolean games with costs. These are games in which each player assigns truth-values to a set of propositional variables she uniquely
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Chaperot, Benoit, and Colin Fyfe. "Improving Artificial Intelligence In a Motocross Game." In 2006 IEEE Symposium on Computational Intelligence and Games. IEEE, 2006. http://dx.doi.org/10.1109/cig.2006.311698.

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Siljebråt, Henrik, Caspar Addyman, and Alan Pickering. "Towards human-like artificial intelligence using StarCraft 2." In FDG '18: Foundations of Digital Games 2018. ACM, 2018. http://dx.doi.org/10.1145/3235765.3235811.

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Maubert, Bastien, Sophie Pinchinat, Francois Schwarzentruber, and Silvia Stranieri. "Concurrent Games in Dynamic Epistemic Logic." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/260.

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Action models of Dynamic Epistemic Logic (DEL) represent precisely how actions are perceived by agents. DEL has recently been used to define infinite multi-player games, and it was shown that they can be solved in some cases. However, the dynamics being defined by the classic DEL update product for individual actions, only turn-based games have been considered so far. In this work we define a concurrent DEL product, propose a mechanism to resolve conflicts between actions, and define concurrent DEL games. As in the turn-based case, the obtained concurrent infinite game arenas can be finitely r
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Liu, Tianyu, Zijie Zheng, Hongchang Li, Kaigui Bian, and Lingyang Song. "Playing Card-Based RTS Games with Deep Reinforcement Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/631.

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Game AI is of great importance as games are simulations of reality. Recent research on game AI has shown much progress in various kinds of games, such as console games, board games and MOBA games. However, the exploration in RTS games remains a challenge for their huge state space, imperfect information, sparse rewards and various strategies. Besides, the typical card-based RTS games have complex card features and are still lacking solutions. We present a deep model SEAT (selection-attention) to play card-based RTS games. The SEAT model includes two parts, a selection part for card choice and
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Cermak, Jiri, Branislav Bošanský, and Viliam Lisý. "An Algorithm for Constructing and Solving Imperfect Recall Abstractions of Large Extensive-Form Games." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/130.

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We solve large two-player zero-sum extensive-form games with perfect recall. We propose a new algorithm based on fictitious play that significantly reduces memory requirements for storing average strategies. The key feature is exploiting imperfect recall abstractions while preserving the convergence rate and guarantees of fictitious play applied directly to the perfect recall game. The algorithm creates a coarse imperfect recall abstraction of the perfect recall game and automatically refines its information set structure only where the imperfect recall might cause problems. Experimental evalu
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Ben Amor, Nahla, Helene Fargier, and Régis Sabbadin. "Equilibria in Ordinal Games: A Framework based on Possibility Theory." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/16.

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The present paper proposes the first definition of mixed equilibrium for ordinal games. This definition naturally extends possibilistic (single agent) decision theory. This allows us to provide a unifying view of single and multi-agent qualitative decision theory. Our first contribution is to show that ordinal games always admit a possibilistic mixed equilibrium, which can be seen as a qualitative counterpart to mixed (probabilistic) equilibrium.Then, we show that a possibilistic mixed equilibrium can be computed in polynomial time (wrt the size of the game), which contrasts with pure Nash or
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Reports on the topic "Artificial Intelligence on games"

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Rodin, Ervin Y. Artificial Intelligence Methods in Pursuit Evasion Differential Games. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada227366.

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Rodin, Ervin Y. Artificial Intelligence Methodologies in Flight Related Differential Game, Control and Optimization Problems. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada262405.

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Novak, Jr, Simmons Gordon S., Porter Robert F., Kumar Bruce W., Causey Vipin, and Robert L. Artificial Intelligence Project. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada230793.

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Lesser, Victor R., Paul Cohen, and Wendy Lehnert. Center for Artificial Intelligence. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada282272.

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Boros, E., P. L. Hammer, and F. S. Roberts. Optimization and Artificial Intelligence. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada311365.

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Lessor, Victor R., Paul Cohen, and Wendy Lehnert. Center for Artificial Intelligence. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada275812.

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Gowens, J. W. Applied Artificial Intelligence Seminar. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada268571.

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Stevenson, Charles A. Artificial Intelligence and Expert Systems. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada436516.

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Aghion, Philippe, Benjamin Jones, and Charles Jones. Artificial Intelligence and Economic Growth. National Bureau of Economic Research, 2017. http://dx.doi.org/10.3386/w23928.

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Acemoglu, Daron, and Pascual Restrepo. Artificial Intelligence, Automation and Work. National Bureau of Economic Research, 2018. http://dx.doi.org/10.3386/w24196.

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