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Journal articles on the topic 'Artificial Intelligence on games'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

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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2

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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3

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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4

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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6

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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7

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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9

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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11

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’

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12

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 (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 intelli

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13

Downey, Shaun, and Darryl Charles. "Distribution of Artificial Intelligence in Digital Games." International Journal of Intelligent Information Technologies 11, no. 3 (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 heuris

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14

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

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15

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

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16

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

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17

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

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18

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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19

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

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20

Duarte, Fernando Fradique, Nuno Lau, Artur Pereira, and Luis Paulo Reis. "A Survey of Planning and Learning in Games." Applied Sciences 10, no. 13 (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

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21

Barnes, Tiffany, Oliver Bown, Michael Buro, et al. "Reports of the Workshops Held at the Tenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment." AI Magazine 36, no. 1 (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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22

Barot, Camille, Michael Buro, Michael Cook, et al. "The AIIDE 2015 Workshop Program." AI Magazine 37, no. 2 (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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23

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 h

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24

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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25

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

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26

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

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27

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 (2015): 317–35. http://dx.doi.org/10.1109/tciaig.2014.2339221.

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28

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

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29

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

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30

Kamble, Rupali, and Deepali Shah. "APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN HUMAN LIFE." International Journal of Research -GRANTHAALAYAH 6, no. 6 (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

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31

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

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32

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 to

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33

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 intr

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34

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 selfish

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35

BONZON, ELISE, CAROLINE DEVRED, and MARIE-CHRISTINE LAGASQUIE-SCHIEX. "ARGUMENTATION AND CP-BOOLEAN GAMES." International Journal on Artificial Intelligence Tools 19, no. 04 (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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36

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 (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 pa

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37

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

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38

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

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39

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

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40

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. Un

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41

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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42

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

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43

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

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44

Bown, Oliver, Arne Eigenfeldt, Rania Hodhod, et al. "Reports on the 2012 AIIDE Workshops." AI Magazine 34, no. 1 (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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45

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

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46

Yannakakis, Georgios N., and John Hallam. "TOWARDS OPTIMIZING ENTERTAINMENT IN COMPUTER GAMES." Applied Artificial Intelligence 21, no. 10 (2007): 933–71. http://dx.doi.org/10.1080/08839510701527580.

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47

Bachrach, Yoram, and Jeffrey S. Rosenschein. "Power in threshold network flow games." Autonomous Agents and Multi-Agent Systems 18, no. 1 (2008): 106–32. http://dx.doi.org/10.1007/s10458-008-9057-6.

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48

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

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49

Koyyalagunta, Divya, Anna Sun, Rachel Lea Draelos, and Cynthia Rudin. "Playing Codenames with Language Graphs and Word Embeddings." Journal of Artificial Intelligence Research 71 (June 23, 2021): 319–46. http://dx.doi.org/10.1613/jair.1.12665.

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Although board games and video games have been studied for decades in artificial intelligence research, challenging word games remain relatively unexplored. Word games are not as constrained as games like chess or poker. Instead, word game strategy is defined by the players’ understanding of the way words relate to each other. The word game Codenames provides a unique opportunity to investigate common sense understanding of relationships between words, an important open challenge. We propose an algorithm that can generate Codenames clues from the language graph BabelNet or from any of several

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Bowling, Michael, Johannes Fürnkranz, Thore Graepel, and Ron Musick. "Machine learning and games." Machine Learning 63, no. 3 (2006): 211–15. http://dx.doi.org/10.1007/s10994-006-8919-x.

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