Journal articles: 'Artificial intelligence in games'

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Lucas, Simon. "Artificial Intelligence and Games." KI - Künstliche Intelligenz 34, no. 1 (February 17, 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 (January 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, Peter Clark, Huáscar Espinoza, Eitan Farchi, Christopher Geib, et al. "Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence." AI Magazine 40, no. 3 (September 30, 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 Intelligence, Knowledge Extraction from Games, Network Interpretability for Deep Learning, Plan, Activity, and Intent Recognition, Reasoning and Learning for Human-Machine Dialogues, Reasoning for Complex Question Answering, Recommender Systems Meet Natural Language Processing, Reinforcement Learning in Games, and Reproducible AI. This report contains brief summaries of the all the workshops that were held.

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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 (March 19, 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 games is made to identify patterns for AI in games which include decision trees, scripted behaviour and learning agents.

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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 (September 18, 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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Hanley, John T. "GAMES, game theory and artificial intelligence." Journal of Defense Analytics and Logistics 5, no. 2 (December 7, 2021): 114–30. http://dx.doi.org/10.1108/jdal-10-2021-0011.

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PurposeThe purpose of this paper is to illustrate how game theoretic solution concepts inform what classes of problems will be amenable to artificial intelligence and machine learning (AI/ML), and how to evolve the interaction between human and artificial intelligence.Design/methodology/approachThe approach addresses the development of operational gaming to support planning and decision making. It then provides a succinct summary of game theory for those designing and using games, with an emphasis on information conditions and solution concepts. It addresses how experimentation demonstrates where human decisions differ from game theoretic solution concepts and how games have been used to develop AI/ML. It concludes by suggesting what classes of problems will be amenable to AI/ML, and which will not. It goes on to propose a method for evolving human/artificial intelligence.FindingsGame theoretic solution concepts inform classes of problems where AI/ML 'solutions' will be suspect. The complexity of the subject requires a campaign of learning.Originality/valueThough games have been essential to the development of AI/ML, practitioners have yet to employ game theory to understand its limitations.

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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 (July 31, 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 The goal of this paper is to use artificial intelligence research to acquire more extensive knowledge of ourselves. On the one hand, we provide a philosophical background to facilitate this, and on the other hand, we try to improve the social acceptance of artificial intelligence. We argue that the way to maintain and further develop human culture is through gaming and artificial intelligence. In support of this thesis we make an attempt to create a theory of subjectivity. Keywords: artificial intelligence, complexity, entropy, meme, computer games, esport

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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 and complete logical system that describes the interplay between the coalition power modality with intelligence and the distributed knowledge modality in games with imperfect information.

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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 (March 27, 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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Franchi, Stefano. "Chess, Games, and Flies." Essays in Philosophy 6, no. 1 (2005): 85–114. http://dx.doi.org/10.5840/eip20056119.

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Research in Artificial Intelligence has always had a very strong relationship with games and game-playing, and especially with chess. Workers in AI have always denied that this interest was more than purely accidental. Parlor games, they claimed, became a favorite topic of interest because they provided the ideal test case for any simulation of intelligence. Chess is the Drosophila of AI, it was said, with reference to the fruit-fly whose fast reproductive cycle made it into a favorite test bed for genetic theories for almost a century. In this paper I will try to show Artificial Intelligence’s relationship to games is quite different from what this analogy suggests. In fact, I will argue that AI is, at core, a theory of games and a theory of subjectivity as game-playing.

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Vallade, Benoît, Alexandre David, and Tomoharu Nakashima. "Three Layers Framework Concept for Adjustable Artificial Intelligence." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 6 (November 20, 2015): 867–79. http://dx.doi.org/10.20965/jaciii.2015.p0867.

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This paper proposes a concept of layered framework for adjustable artificial intelligence. Artificial intelligences are used in various areas of computer science for decision making tasks. Traditionally artificial intelligences are developed in order to be used for a specific purpose within a particular software. However, this paper stands as the first step of a research in progress whose final objective is to design an artificial intelligence adjustable to every types of problems without any modification in its source code. The present work focuses on a framework of such an artificial intelligence and is conducted in the context of video games. This framework, composed of three layers, would be re-usable for all types of game.

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Downey, Shaun, and Darryl Charles. "Distribution of Artificial Intelligence in Digital Games." International Journal of Intelligent Information Technologies 11, no. 3 (July 2015): 1–14. http://dx.doi.org/10.4018/ijiit.2015070101.

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This paper reports on the creation of an application capable of producing intelligent character behavior based on distribution of Artificial Intelligence. To develop, test and experiment on applied Artificial Intelligence, computer games are quickly becoming the ideal simulation test bed for the implementation of computer generated AI. The application of Artificial Intelligence algorithms create immersive game-play where human players can interact with non-player characters and interactions with the environment helps shape the way in which games are played. A-Star pathfinding utilizes a heuristic function implementing cost of moving through a virtual world, this actively affects how an agent responds to a situation and can alter its decision making. This paper describes the implementation of the A-Star algorithm combined with gameplay mechanics used to simulate multi-agent communication within a randomly generated game-world.

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Miakisz, Katarzyna, Edward W. Piotrowski, and Jan Sładkowski. "Quantization of games: Towards quantum artificial intelligence." Theoretical Computer Science 358, no. 1 (July 2006): 15–22. http://dx.doi.org/10.1016/j.tcs.2005.11.003.

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Grossi, Davide, and Paolo Turrini. "Dependence in games and dependence games." Autonomous Agents and Multi-Agent Systems 25, no. 2 (June 24, 2011): 284–312. http://dx.doi.org/10.1007/s10458-011-9176-3.

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Aiolli, Fabio, and Claudio E. Palazzi. "Enhancing Artificial Intelligence on a Real Mobile Game." International Journal of Computer Games Technology 2009 (2009): 1–9. http://dx.doi.org/10.1155/2009/456169.

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Mobile games represent a killer application that is attracting millions of subscribers worldwide. One of the aspects crucial to the commercial success of a game is ensuring an appropriately challenging artificial intelligence (AI) algorithm against which to play. However, creating this component is particularly complex as classic search AI algorithms cannot be employed by limited devices such as mobile phones or, even on more powerful computers, when considering imperfect information games (i.e., games in which participants do not a complete knowledge of the game state at any moment). In this paper, we propose to solve this issue by resorting to a machine learning algorithm which uses profiling functionalities in order to infer the missing information, thus making the AI able to efficiently adapt its strategies to the human opponent. We studied a simple and computationally light machine learning method that can be employed with success, enabling AI improvements for imperfect information games even on mobile phones. We created a mobile phone-based version of a game calledGhostsand present results which clearly show the ability of our algorithm to quickly improve its own predictive performance as far as the number of games against the same human opponent increases.

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Sanjaya, Muhammad Fachri, Heny Pratiwi, and Pitrasacha Adytia. "Application of the Finite State Machine Method in the Desktop-Based “Heroes Of Dawn” RPG Turn-Based Game." TEPIAN 2, no. 2 (June 1, 2021): 69–73. http://dx.doi.org/10.51967/tepian.v2i2.348.

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FSM (Finite State Machine) is a method of implementing artificial intelligence that is applied to make a decision on NPC (Non Player Character). The application of FSM that is often encountered is to form an NPC with intelligence, so that the NPC can respond to the player's character so that the NPC seems to be able to think. Games have various types (genres) and are increasingly varied in line with the development of hardware and software technology. Writing will focus on games with the Role Playing Game genre or often called RPG. Games in general use Artifical Intelligence in their systems to make the game more interesting to play. Artifical Intelligence is usually applied to NPC (Non Player Character) / Enemy in the game or opponents who must be defeated, one of the applications of Artifical Intelligence in the game to be used in this research is the Finite State Machine (FSM) method.

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Duarte, Fernando Fradique, Nuno Lau, Artur Pereira, and Luis Paulo Reis. "A Survey of Planning and Learning in Games." Applied Sciences 10, no. 13 (June 30, 2020): 4529. http://dx.doi.org/10.3390/app10134529.

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In general, games pose interesting and complex problems for the implementation of intelligent agents and are a popular domain in the study of artificial intelligence. In fact, games have been at the center of some of the most well-known achievements in artificial intelligence. From classical board games such as chess, checkers, backgammon and Go, to video games such as Dota 2 and StarCraft II, artificial intelligence research has devised computer programs that can play at the level of a human master and even at a human world champion level. Planning and learning, two well-known and successful paradigms of artificial intelligence, have greatly contributed to these achievements. Although representing distinct approaches, planning and learning try to solve similar problems and share some similarities. They can even complement each other. This has led to research on methodologies to combine the strengths of both approaches to derive better solutions. This paper presents a survey of the multiple methodologies that have been proposed to integrate planning and learning in the context of games. In order to provide a richer contextualization, the paper also presents learning and planning techniques commonly used in games, both in terms of their theoretical foundations and applications.

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Barbe, Lou, Cendrine Mony, and Benjamin W. Abbott. "Artificial Intelligence Accidentally Learned Ecology through Video Games." Trends in Ecology & Evolution 35, no. 7 (July 2020): 557–60. http://dx.doi.org/10.1016/j.tree.2020.04.006.

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Liu, Zhenghao. "Application of Artificial Intelligence Technology in Basketball Games." IOP Conference Series: Materials Science and Engineering 750 (March 24, 2020): 012093. http://dx.doi.org/10.1088/1757-899x/750/1/012093.

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Cui, Guo Wen, and Xin Qiang Li. "Simulation Analysis of Mind Sports Games Based on Artificial Intelligence." Advanced Materials Research 989-994 (July 2014): 2065–69. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2065.

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Mind sports games is rose in recent years, which is the comprehensive sports event taking the chess sports as the main part, and it is a perfect combination of human intelligence and sports art. Due to a simple history of mind sports games, there exists some problems that are the imbalance of development, lack of funds, and event organization difficulty, which has restricted the rapid development of mind sports games. Aiming at the problems of mind sports games encountering in the development, the paper first proposes the coupling development mechanism for the organization of mind sports games and its development mechanism, and then establishes the model of development mechanism based on the coupling reduction mechanism, and the model has been made simulation analysis. At last, the results show that the application of this coupling model can find the bottleneck of the development of mind sports games, so as to open the breakthrough point for its development, and to improve its development level.

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Barnes, Tiffany, Oliver Bown, Michael Buro, Michael Cook, Arne Eigenfeldt, Héctor Muñoz-Avila, Santiago Ontañón, et al. "Reports of the Workshops Held at the Tenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment." AI Magazine 36, no. 1 (March 25, 2015): 99–102. http://dx.doi.org/10.1609/aimag.v36i1.2576.

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The AIIDE-14 Workshop program was held Friday and Saturday, October 3–4, 2014 at North Carolina State University in Raleigh, North Carolina. The workshop program included five workshops covering a wide range of topics. The titles of the workshops held Friday were Games and Natural Language Processing, and Artificial Intelligence in Adversarial Real-Time Games. The titles of the workshops held Saturday were Diversity in Games Research, Experimental Artificial Intelligence in Games, and Musical Metacreation. This article presents short summaries of those events.

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Straeubig, Michael. "Games, AI and Systems." Eludamos: Journal for Computer Game Culture 10, no. 1 (April 21, 2020): 141–60. http://dx.doi.org/10.7557/23.6176.

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In recent years, we have observed impressive advancements at the intersection of games and artificial intelligence. Often these developments are described in terms of technological progress, while public discourses on their cultural, social and political impact are largely decoupled. I present an alternative rhetoric by speculating about the emergence of AI within social systems. In a radical departure from the dominant discourse, I describe seven roles - Mechanic, Alter/Ego, Observer, Protector, Player, Creator and God - that an AI may assume in the environment of videogames. I reflect on the ramifications of these roles for the idea of an artificial general intelligence (AGI), mainly hoping to irritate the prevailing discussion.

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Barot, Camille, Michael Buro, Michael Cook, Mirjam Palosaari Eladhari, Boyang “Albert” Li, Antonios Liapis, Magnus Johansson, et al. "The AIIDE 2015 Workshop Program." AI Magazine 37, no. 2 (July 4, 2016): 91–94. http://dx.doi.org/10.1609/aimag.v37i2.2660.

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The workshop program at the Eleventh Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment was held November 14–15, 2015 at the University of California, Santa Cruz, USA. The program included 4 workshops (one of which was a joint workshop): Artificial Intelligence in Adversarial Real-Time Games, Experimental AI in Games, Intelligent Narrative Technologies and Social Believability in Games, and Player Modeling. This article contains the reports of three of the four workshops.

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Safadi, Firas, Raphael Fonteneau, and Damien Ernst. "Artificial Intelligence in Video Games: Towards a Unified Framework." International Journal of Computer Games Technology 2015 (2015): 1–30. http://dx.doi.org/10.1155/2015/271296.

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With modern video games frequently featuring sophisticated and realistic environments, the need for smart and comprehensive agents that understand the various aspects of complex environments is pressing. Since video game AI is often specifically designed for each game, video game AI tools currently focus on allowing video game developers to quickly and efficiently create specific AI. One issue with this approach is that it does not efficiently exploit the numerous similarities that exist between video games not only of the same genre, but of different genres too, resulting in a difficulty to handle the many aspects of a complex environment independently for each video game. Inspired by the human ability to detect analogies between games and apply similar behavior on a conceptual level, this paper suggests an approach based on the use of a unified conceptual framework to enable the development of conceptual AI which relies on conceptual views and actions to define basic yet reasonable and robust behavior. The approach is illustrated using two video games,RavenandStarCraft: Brood War.

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Sari, Junia Melya, and Edi Purwanta. "The Implementation of Artificial Intelligence in STEM-Based Creative Learning in the Society 5.0 Era." Tadris: Jurnal Keguruan dan Ilmu Tarbiyah 6, no. 2 (December 31, 2021): 433–40. http://dx.doi.org/10.24042/tadris.v6i2.10135.

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This research aimed at utilizing artificial intelligence in STEM-based creative learning in the society 5.0 era. The researchers investigated how an educator can utilize artificial intelligence and optimize it into a STEM-based learning process. STEM stands for Science, Technology, Engineering, and Math. The United States initiated it to combine the four disciplines integrated into a problem-based learning method and everyday contextual events. Artificial intelligence is an intelligence added to a system managed in a scientific context. Artificial intelligence is created and put into a machine (computer) to do work like humans. Several fields that use artificial intelligence include expert systems, computer games (games), fuzzy logic, artificial neural networks, and robotics. The researchers employed the literature review or library research by reviewing the results of various studies and collecting data from assorted references and sources. In conclusion, implementing artificial intelligence in STEM-based creative learning can be an alternative for an educator in the learning process. Artificial intelligence (AI) is expected to help educators in the creative learning process by implementing long-life education and showing behavioral changes in a better direction cognitively, affectively, and psychometrically, especially in the era of society 5.0.

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Sadeghi Esfahlani, Shabnam, Javaid Butt, and Hassan Shirvani. "Fusion of Artificial Intelligence in Neuro-Rehabilitation Video Games." IEEE Access 7 (2019): 102617–27. http://dx.doi.org/10.1109/access.2019.2926118.

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Levene, Mark. "Artificial Intelligence for Games. Series in Interactive 3D Technology." Computer Journal 50, no. 3 (December 8, 2006): 371. http://dx.doi.org/10.1093/comjnl/bxl071.

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Ernandes, Marco. "Artificial Intelligence & Games: Should Computational Psychology be Revalued?" Topoi 24, no. 2 (September 2005): 229–42. http://dx.doi.org/10.1007/s11245-005-5090-0.

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Yannakakis, Georgios N., and Julian Togelius. "A Panorama of Artificial and Computational Intelligence in Games." IEEE Transactions on Computational Intelligence and AI in Games 7, no. 4 (December 2015): 317–35. http://dx.doi.org/10.1109/tciaig.2014.2339221.

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Wernerfelt, Birger. "Semifuzzy games." Fuzzy Sets and Systems 19, no. 1 (May 1986): 21–28. http://dx.doi.org/10.1016/s0165-0114(86)80074-4.

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Guha, Shibashis, Orna Kupferman, and Gal Vardi. "Multi-player flow games." Autonomous Agents and Multi-Agent Systems 33, no. 6 (August 24, 2019): 798–820. http://dx.doi.org/10.1007/s10458-019-09420-2.

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Kamble, Rupali, and Deepali Shah. "APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN HUMAN LIFE." International Journal of Research -GRANTHAALAYAH 6, no. 6 (June 30, 2018): 178–88. http://dx.doi.org/10.29121/granthaalayah.v6.i6.2018.1363.

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Artificial Intelligence (AI) has revolutionized in information technology.AI is a subfield of computer science that includes the creation of intelligent machines and software that work and react like human beings. AI and its Applications gets used in various fields of life of humans as expert system solve the complex problems in various areas as science, engineering, business, medicine, video games and Advertising. But “Do any traffic lights use Artificial Intelligence??”, I thought a lot of this when waiting in a red light. This paper gives an overview of Artificial Intelligence and its applications used in human life. This will explore the current use of Artificial Intelligence technologies in Network Intrusion for protecting computer and communication networks from intruders, in the medical area-medicine, to improve hospital inpatient care, for medical image classification, in the accounting databases to mitigate the problems of it, in the computer games, and in Advertising. Also, it will show artificial intelligence principle and how they were applying in traffic signal control, how they solve some traffic problem in actual. This paper gives an introduction to a self-learning system based on RBF neural network and how the system can simulate the traffic police’s experience. This paper is focusing on how to evaluate the effect of the control with the changing of the traffic and adjust the signal with the different techniques of Artificial Intelligence.

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Monderer, Dov, Moshe Tennenholtz, and Hal Varian. "Economics and Artificial Intelligence." Games and Economic Behavior 35, no. 1-2 (April 2001): 1–5. http://dx.doi.org/10.1006/game.2001.0848.

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Ortiz, Luis. "On Sparse Discretization for Graphical Games." Journal of Artificial Intelligence Research 69 (September 15, 2020): 67–84. http://dx.doi.org/10.1613/jair.1.12391.

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Graphical games are one of the earliest examples of the impact that the general field of graphical models have had in other areas, and in this particular case, in classical mathematical models in game theory. Graphical multi-hypermatrix games, a concept formally introduced in this research note, generalize graphical games while allowing the possibility of further space savings in model representation to that of standard graphical games. The main focus of this research note is discretization schemes for computing approximate Nash equilibria, with emphasis on graphical games, but also briefly touching on normal-form and polymatrix games. The main technical contribution is a theorem that establishes sufficient conditions for a discretization of the players’ space of mixed strategies to contain an approximate Nash equilibrium. The result is actually stronger because every exact Nash equilibrium has a nearby approximate Nash equilibrium on the grid induced by the discretization. The sufficient conditions are weaker than those of previous results. In particular, a uniform discretization of size linear in the inverse of the approximation error and in the natural game-representation parameters suffices. The theorem holds for a generalization of graphical games, introduced here. The result has already been useful in the design and analysis of tractable algorithms for graphical games with parametric payoff functions and certain game-graph structures. For standard graphical games, under natural conditions, the discretization is logarithmic in the game-representation size, a substantial improvement over the linear dependency previously required. Combining the improved discretization result with old results on constraint networks in AI simplifies the derivation and analysis of algorithms for computing approximate Nash equilibria in graphical games.

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Chalkiadakis, G., E. Elkind, E. Markakis, M. Polukarov, and N. R. Jennings. "Cooperative Games with Overlapping Coalitions." Journal of Artificial Intelligence Research 39 (September 24, 2010): 179–216. http://dx.doi.org/10.1613/jair.3075.

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In the usual models of cooperative game theory, the outcome of a coalition formation process is either the grand coalition or a coalition structure that consists of disjoint coalitions. However, in many domains where coalitions are associated with tasks, an agent may be involved in executing more than one task, and thus may distribute his resources among several coalitions. To tackle such scenarios, we introduce a model for cooperative games with overlapping coalitionsor overlapping coalition formation (OCF) games. We then explore the issue of stability in this setting. In particular, we introduce a notion of the core, which generalizes the corresponding notion in the traditional (non-overlapping) scenario. Then, under some quite general conditions, we characterize the elements of the core, and show that any element of the core maximizes the social welfare. We also introduce a concept of balancedness for overlapping coalitional games, and use it to characterize coalition structures that can be extended to elements of the core. Finally, we generalize the notion of convexity to our setting, and show that under some natural assumptions convex games have a non-empty core. Moreover, we introduce two alternative notions of stability in OCF that allow a wider range of deviations, and explore the relationships among the corresponding definitions of the core, as well as the classic (non-overlapping) core and the Aubin core. We illustrate the general properties of the three cores, and also study them from a computational perspective, thus obtaining additional insights into their fundamental structure.

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Apt, K. R., and G. Schaefer. "Selfishness Level of Strategic Games." Journal of Artificial Intelligence Research 49 (February 17, 2014): 207–40. http://dx.doi.org/10.1613/jair.4164.

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We introduce a new measure of the discrepancy in strategic games between the social welfare in a Nash equilibrium and in a social optimum, that we call selfishness level. It is the smallest fraction of the social welfare that needs to be offered to each player to achieve that a social optimum is realized in a pure Nash equilibrium. The selfishness level is unrelated to the price of stability and the price of anarchy and is invariant under positive linear transformations of the payoff functions. Also, it naturally applies to other solution concepts and other forms of games. We study the selfishness level of several well-known strategic games. This allows us to quantify the implicit tension within a game between players' individual interests and the impact of their decisions on the society as a whole. Our analyses reveal that the selfishness level often provides a deeper understanding of the characteristics of the underlying game that influence the players' willingness to cooperate. In particular, the selfishness level of finite ordinal potential games is finite, while that of weakly acyclic games can be infinite. We derive explicit bounds on the selfishness level of fair cost sharing games and linear congestion games, which depend on specific parameters of the underlying game but are independent of the number of players. Further, we show that the selfishness level of the $n$-players Prisoner's Dilemma is c/(b(n-1)-c), where b and c are the benefit and cost for cooperation, respectively, that of the n-players public goods game is (1-c/n)/(c-1), where c is the public good multiplier, and that of the Traveler's Dilemma game is (b-1)/2, where b is the bonus. Finally, the selfishness level of Cournot competition (an example of an infinite ordinal potential game), Tragedy of the Commons, and Bertrand competition is infinite.

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BONZON, ELISE, CAROLINE DEVRED, and MARIE-CHRISTINE LAGASQUIE-SCHIEX. "ARGUMENTATION AND CP-BOOLEAN GAMES." International Journal on Artificial Intelligence Tools 19, no. 04 (August 2010): 487–510. http://dx.doi.org/10.1142/s0218213010000297.

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There already exist some links between argumentation and game theory. For instance, dynamic games can be used for simulating interactions between agents in an argumentation process. In this paper, we establish a new link between these domains in a static framework: we show how an argumentation framework can be translated into a CP-Boolean game and how this translation can be used for computing extensions of argumentation semantics. We give formal algorithms to do so.

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TAN, CHARLIE IRAWAN, CHANG-MIN CHEN, WEN-KAI TAI, and CHIN-CHEN CHANG. "PATH PLANNING FOR RACING GAMES." International Journal on Artificial Intelligence Tools 19, no. 05 (October 2010): 679–702. http://dx.doi.org/10.1142/s0218213010000364.

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We propose a set of path planning tools including path generator, cost map generator, and path editor for racing games. The user can define the race by providing a racetrack as a 3D model and weights of the devised turn and heuristic functions in our system. Then, the proposed cost map generator automatically generates necessary information of the racetrack including cost map and distance to finish of any position on the race track. Different from the traditional A* problem, in our research the obstacles are dynamic and there are multiple sources and destinations. Our approach generates the path of each racer on the basis of time slots to which the path finding method applies on the fly. To further guarantee the quality of the path, we implement path smoothing using a Gaussian filter and provide an off-line path editor that allows users to edit the path in time-space domain intuitively, flexibly, and effectively. Our tools have been verified in a horse racing game to generate natural racer behaviors, demonstrating realistic and exciting racing.

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Dignum, Frank. "Agents for games and simulations." Autonomous Agents and Multi-Agent Systems 24, no. 2 (March 23, 2011): 217–20. http://dx.doi.org/10.1007/s10458-011-9169-2.

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Sakawa, Masatoshi, and Ichiro Nishizaki. "Fuzzy cooperative games." Fuzzy Sets and Systems 139, no. 2 (October 2003): 463–64. http://dx.doi.org/10.1016/s0165-0114(03)00035-6.

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Łupkowski, Paweł, and Andrzej Wiśniewski. "Turing Interrogative Games." Minds and Machines 21, no. 3 (March 24, 2011): 435–48. http://dx.doi.org/10.1007/s11023-011-9245-z.

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Crandall, J. W. "Towards Minimizing Disappointment in Repeated Games." Journal of Artificial Intelligence Research 49 (February 10, 2014): 111–42. http://dx.doi.org/10.1613/jair.4202.

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We consider the problem of learning in repeated games against arbitrary associates. Specifically, we study the ability of expert algorithms to quickly learn effective strategies in repeated games, towards the ultimate goal of learning near-optimal behavior against any arbitrary associate within only a handful of interactions. Our contribution is three-fold. First, we advocate a new metric, called disappointment, for evaluating expert algorithms in repeated games. Unlike minimizing traditional notions of regret, minimizing disappointment in repeated games is equivalent to maximizing payoffs. Unfortunately, eliminating disappointment is impossible to guarantee in general. However, it is possible for an expert algorithm to quickly achieve low disappointment against many known classes of algorithms in many games. Second, we show that popular existing expert algorithms often fail to achieve low disappointment against a variety of associates, particularly in early rounds of the game. Finally, we describe a new meta-algorithm that can be applied to existing expert algorithms to substantially reduce disappointment in many two-player repeated games when associates follow various static, reinforcement learning, and expert algorithms.

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Fujita, Hamido, and I.-Chen Wu. "A special issue on artificial intelligence in computer games: AICG." Knowledge-Based Systems 34 (October 2012): 1–2. http://dx.doi.org/10.1016/j.knosys.2012.05.014.

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García-Sánchez, Pablo. "Georgios N. Yannakakis and Julian Togelius: Artificial Intelligence and Games." Genetic Programming and Evolvable Machines 20, no. 1 (September 11, 2018): 143–45. http://dx.doi.org/10.1007/s10710-018-9337-0.

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Marcotte, Ryan, and Howard J. Hamilton. "Behavior Trees for Modelling Artificial Intelligence in Games: A Tutorial." Computer Games Journal 6, no. 3 (July 25, 2017): 171–84. http://dx.doi.org/10.1007/s40869-017-0040-9.

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Bown, Oliver, Arne Eigenfeldt, Rania Hodhod, Philippe Pasquier, Reid Swanson, Stephen G. Ware, and Jichen Zhu. "Reports on the 2012 AIIDE Workshops." AI Magazine 34, no. 1 (December 18, 2012): 90. http://dx.doi.org/10.1609/aimag.v34i1.2459.

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The 2012 AIIDE Conference included four workshops: Artificial Intelligence in Adversarial Real-Time Games, Human Computation in Deigital Entertainment and AI for Serious Games, Intelligent Narrative Technologies, and Musican Metacreation. The workshops took place October 8-9, 2012 at Stanford University. This report contains summaries of the activities of those four workshops.

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Bruder, Johannes. "Donkey Kong's Legacy." TSANTSA – Journal of the Swiss Anthropological Association 26 (June 30, 2021): 71–84. http://dx.doi.org/10.36950/tsantsa.2021.26.6972.

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The article discusses forms of contamination between human and artificial intelligence in computational neuroscience and machine learning research. I begin with a deep dive into an experiment with the legacy microprocessor MOS 6502, conducted by two engineers working in computational neuroscience, to explain why and how machine learning algorithms are increasingly employed to simulate human cognition and behavior. Through the strategic use of the microprocessor as “model organism” and references to biological and psychological lab research, the authors draw attention to speculative research in machine learning, where arcade video games designed in the 1980s provide test beds for artificial intelligences under development. I elaborate on the politics of these test beds and suggest alternative avenues for machine learning research to avoid that artificial intelligence merely reproduces settler-colonialist politics in silico.

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Bachrach, Y., E. Porat, and J. S. Rosenschein. "Sharing Rewards in Cooperative Connectivity Games." Journal of Artificial Intelligence Research 47 (June 14, 2013): 281–311. http://dx.doi.org/10.1613/jair.3841.

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We consider how selfish agents are likely to share revenues derived from maintaining connectivity between important network servers. We model a network where a failure of one node may disrupt communication between other nodes as a cooperative game called the vertex Connectivity Game (CG). In this game, each agent owns a vertex, and controls all the edges going to and from that vertex. A coalition of agents wins if it fully connects a certain subset of vertices in the graph, called the primary vertices. Power indices measure an agent's ability to affect the outcome of the game. We show that in our domain, such indices can be used to both determine the fair share of the revenues an agent is entitled to, and identify significant possible points of failure affecting the reliability of communication in the network. We show that in general graphs, calculating the Shapley and Banzhaf power indices is #P-complete, but suggest a polynomial algorithm for calculating them in trees. We also investigate finding stable payoff divisions of the revenues in CGs, captured by the game theoretic solution of the core, and its relaxations, the epsilon-core and least core. We show a polynomial algorithm for computing the core of a CG, but show that testing whether an imputation is in the epsilon-core is coNP-complete. Finally, we show that for trees, it is possible to test for epsilon-core imputations in polynomial time.

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Journal articles on the topic 'Artificial intelligence in games'

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