Relevant bibliographies by topics / Atari Video Games

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

Academic literature on the topic 'Atari Video Games'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 July 2025

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Journal articles on the topic "Atari Video Games"

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Zhang, Ruohan, Calen Walshe, Zhuode Liu, et al. "Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6811–20. http://dx.doi.org/10.1609/aaai.v34i04.6161.

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Large-scale public datasets have been shown to benefit research in multiple areas of modern artificial intelligence. For decision-making research that requires human data, high-quality datasets serve as important benchmarks to facilitate the development of new methods by providing a common reproducible standard. Many human decision-making tasks require visual attention to obtain high levels of performance. Therefore, measuring eye movements can provide a rich source of information about the strategies that humans use to solve decision-making tasks. Here, we provide a large-scale, high-quality
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Geffner, Tomas, and Hector Geffner. "Width-Based Planning for General Video-Game Playing." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 11, no. 1 (2021): 23–29. http://dx.doi.org/10.1609/aiide.v11i1.12786.

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IW(1) is a simple search algorithm that assumes that states can be characterized in terms of a set of boolean features or atoms. IW(1) consists of a standard breadth-first search with one variation: a newly generated state is pruned if it does not make a new atom true. Thus, while a breadth-first search runs in time that is exponential in the number of atoms, IW(1) runs in linear time. Variations of the algorithm have been shown to yield state-of-the-art results in classical planning and more recently in the Atari video games. In this paper, we use the algorithm for selecting actions in the ga
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Nyitray, Kristen J. "The Alert Collector: Game On to Game After: Sources for Video Game History." Reference & User Services Quarterly 59, no. 1 (2019): 7. http://dx.doi.org/10.5860/rusq.59.1.7219.

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Kristen Nyitray began her immersion in video games with an Atari 2600 and ColecoVision console and checking out games from her local public library. Later in life, she had the opportunity to start building a video game studies collection in her professional career as an archivist and special collections librarian. While that project has since ended, you get the benefit of her expansive knowledge of video game sources in “Game On to Game After: Sources for Video Game History.” There is much in this column to help librarians wanting to support research in this important entertainment form. Ready
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Reinhard, Andrew. "Excavating Atari: Where the Media was the Archaeology." Journal of Contemporary Archaeology 2, no. 1 (2015): 86–93. https://doi.org/10.1558/jca.v2i1.27108.

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A team of archaeologists excavated Alamogordo, New Mexico’s famed “Atari Burial Ground” in 2014 to media fanfare and public speculation of what archaeology is. This salvage project was the first archaeological dig of an assemblage of video games, in this case thousands of Atari cartridges buried by the company in 1983, quickly becoming the stuff of urban legend. Filmed by documentarians for Microsoft’s Xbox Live, the dig marked a global confluence of science, spectacle, and social media, a literal archaeology of media.
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Williams, Deborah A., and Jack O. Jenkins. "Role of Competitive Anxiety in the Performance of Black College Basketball Players." Perceptual and Motor Skills 63, no. 2 (1986): 847–53. http://dx.doi.org/10.2466/pms.1986.63.2.847.

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Scores on an Atari video game, Martens's Competitive Anxiety Scale and Life Experience Survey, and coaches' ratings of actual performance over 10 home basketball games were obtained for 8 black players and 7 black controls. Several significant ( p ≤ .01) correlations of anxiety and performance with stress were low to moderate and encourage a full-scale study with a larger sample and standardized measures.
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Köster, Raphael, and Martin J. Chadwick. "What can classic Atari video games tell us about the human brain?" Neuron 109, no. 4 (2021): 568–70. http://dx.doi.org/10.1016/j.neuron.2021.01.021.

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Widendaële, Arnaud. "Michael Z. Newman, Atari Age. The Emergence of Video Games in America." 1895, no. 86 (December 1, 2018): 193–96. http://dx.doi.org/10.4000/1895.7192.

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Moretti, Myrna. "Keeping Up With Atari: Neoliberal Expectations in Early Electronics Advertising." TMG Journal for Media History 26, no. 2 (2023): 1–22. http://dx.doi.org/10.18146/tmg.847.

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During the early 1980s, ad campaigns framed purchasing and using emerging consumer electronics as tools for accessing, what Lauren Berlant called, ‘the good life.’ Computers, video games, VCRs, and cassette players might help consumers cultivate a neoliberal, upwardly mobile, and implicitly white, lifestyle. This paper explores early personal computer and home console video game advertisements as a cultural discourse that framed emerging technology through normative gendered, raced, and classed everyday lifestyles in an American context. The central case study is the early 1980s televisual ad
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O'Donnell, Casey. "Atari Age: The Emergence of Video Games in America by Michael Z. Newman." Technology and Culture 63, no. 4 (2022): 1214–15. http://dx.doi.org/10.1353/tech.2022.0172.

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Lin, Jhang, Lee, Lin, and Young. "Using a Reinforcement Q-Learning-Based Deep Neural Network for Playing Video Games." Electronics 8, no. 10 (2019): 1128. http://dx.doi.org/10.3390/electronics8101128.

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This study proposed a reinforcement Q-learning-based deep neural network (RQDNN) that combined a deep principal component analysis network (DPCANet) and Q-learning to determine a playing strategy for video games. Video game images were used as the inputs. The proposed DPCANet was used to initialize the parameters of the convolution kernel and capture the image features automatically. It performs as a deep neural network and requires less computational complexity than traditional convolution neural networks. A reinforcement Q-learning method was used to implement a strategy for playing the vide
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Dissertations / Theses on the topic "Atari Video Games"

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Avverahalli, Ravi Darshan. "Identifying and Prioritizing Critical Information in Military IoT: Video Game Demonstration." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104070.

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Current communication and network systems are not built for delay-sensitive applications. The most obvious fact is that the communication capacity is only achievable in theory with infinitely long codes, which means infinitely long delays. One remedy for this is to use shorter codes. Conceptually, there is a deeper reason for the difficulties in such solutions: in Shannon's original 1948 paper, he started out by stating that the "semantic aspects" of information is "irrelevant" to communications. Hence, in Shannon's communication system, as well as every network built after him, we put all inf
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Naddaf, Yavar. "Game-independent AI agents for playing Atari 2600 console games." Master's thesis, 2010. http://hdl.handle.net/10048/1081.

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Thesis (M.Sc.)--University of Alberta, 2010.<br>Title from PDF file main screen (viewed on July 15, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, Department of Computing Science, University of Alberta. Includes bibliographical references.
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Books on the topic "Atari Video Games"

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Montfort, Nick. Racing the Beam: The Atari Video Computer System. The MIT Press, 2009.

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Fox, Matt. The Video Games Guide. Boxtree Ltd, 2006.

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Weiss, Brett. Classic Home Video Games, 1972-1984: A Complete Reference Guide. McFarland & Company, Inc., Publishers, 2007.

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Meston, Zach. Atari Jaguar: Official Gamer's Guide. Sandwich Islands Publishing, 1995.

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Baer, Ralph H. Videogames: In the Beginning. Rolenta Press, 2005.

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Cazals, Thomas. The video game: Past, present and future. Films for the Humanities & Sciences, 2004.

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Tsai, Mark. Lemmings: The Official Companion, with Exclusive Disk. Prima Games, 1993.

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Sepowski, Stephen J., ed. The Ultimate Hint Book. The Ultimate Game Club Ltd., 1991.

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Staff, Britannica Educational Publishing, and Michael Ray. Gaming: From Atari to Xbox. Rosen Publishing Group, 2011.

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Newman, Michael Z. Atari Age: The Emergence of Video Games in America. MIT Press, 2017.

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Book chapters on the topic "Atari Video Games"

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O’Regan, Gerard. "Atari Video Games." In The Innovation in Computing Companion. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02619-6_9.

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Wardyga, Brian J. "The Atari Generation." In The Video Games Textbook, 2nd ed. CRC Press, 2023. http://dx.doi.org/10.1201/9781003315759-3.

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"Video Games as Computers, Computers as Toys." In Atari Age. The MIT Press, 2017. http://dx.doi.org/10.7551/mitpress/10021.003.0008.

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"Atari and Commodore." In The Golden Age of Video Games. A K Peters/CRC Press, 2011. http://dx.doi.org/10.1201/b10818-14.

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"Introduction: Early Video Games and New Media History." In Atari Age. The MIT Press, 2017. http://dx.doi.org/10.7551/mitpress/10021.003.0004.

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"“Don’t Watch TV Tonight. Play It!” Early Video Games and Television." In Atari Age. The MIT Press, 2017. http://dx.doi.org/10.7551/mitpress/10021.003.0006.

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"Atari, Pong and the Jackals." In The Golden Age of Video Games. A K Peters/CRC Press, 2011. http://dx.doi.org/10.1201/b10818-5.

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Johnston, Andrew R. "Artificial Intelligence and the Temporality of Machine Images." In Media Infrastructures and the Politics of Digital Time. Amsterdam University Press, 2021. http://dx.doi.org/10.5117/9789463727426_ch07.

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DeepMind, a recent artificial intelligence technology created at Google, references in its name the relationship in AI between models of cognition used in this technology‘s development and its new deep learning algorithms. This chapter shows how AI researchers have been attempting to reproduce applied learning strategies in humans but have difficulty accessing and visualizing the computational actions of their algorithms. Google created an interface for engaging with computational temporalities through the production of visual animations based on DeepMind machine-learning test runs of Atari 26
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Payne, Kenneth. "Deep Warbots." In I, Warbot. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197611692.003.0003.

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Machine Learning, and especially Deep Learning, has led to startling advances in AI over the last decade. Much of this research has led by the private sector, rather than the military. Artificial neural networks are highly effective at optimizing decisions in narrow domains, like an Atari video game. That can deliver superhuman performance on some military tasks, like flying robot helicopters. But it may not be sufficient for a more flexible, human-like intelligence, of the sort required to make strategic judgments about escalation.
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Conference papers on the topic "Atari Video Games"

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Kelly, Stephen, and Malcolm I. Heywood. "Multi-task learning in Atari video games with emergent tangled program graphs." In GECCO '17: Genetic and Evolutionary Computation Conference. ACM, 2017. http://dx.doi.org/10.1145/3071178.3071303.

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Goyal, Prasoon, Scott Niekum, and Raymond J. Mooney. "Using Natural Language for Reward Shaping in 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.2019/331.

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Recent reinforcement learning (RL) approaches have shown strong performance in complex domains, such as Atari games, but are highly sample inefficient. A common approach to reduce interaction time with the environment is to use reward shaping, which involves carefully designing reward functions that provide the agent intermediate rewards for progress towards the goal. Designing such rewards remains a challenge, though. In this work, we use natural language instructions to perform reward shaping. We propose a framework that maps free-form natural language instructions to intermediate rewards, t
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Kelly, Stephen, and Malcolm Heywood. "Emergent Tangled Program Graphs in Multi-Task Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/740.

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We propose a Genetic Programming (GP) framework to address high-dimensional Multi-Task Reinforcement Learning (MTRL) through emergent modularity. A bottom-up process is assumed in which multiple programs self-organize into collective decision-making entities, or teams, which then further develop into multi-team policy graphs, or Tangled Program Graphs (TPG). The framework learns to play three Atari video games simultaneously, producing a single control policy that matches or exceeds leading results from (game-specific) deep reinforcement learning in each game. More importantly, unlike the repr
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Zigon, Bob, and Fengguang Song. "Accelerating Experience Replay for Deep Q-Networks with Reduced Target Computation." In International Conference on Computer Science and Machine Learning (CSML 2023). Academy and Industry Research Collaboration Center (AIRCC), 2023. http://dx.doi.org/10.5121/csit.2023.130101.

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Mnih’s seminal deep reinforcement learning paper that applied a Deep Q-network to Atari video games demonstrated the importance of a replay buffer and a target network. Though the pair were required for convergence, the use of the replay buffer came at a significant computational cost. With each new sample generated by the system, the targets in the mini batch buffer were continually recomputed. We propose an alternative that eliminates the target recomputation called TAO-DQN (Target Accelerated Optimization-DQN). Our approach focuses on a new replay buffer algorithm that lowers the computatio
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Wang, Lun, Zaynah Javed, Xian Wu, Wenbo Guo, Xinyu Xing, and Dawn Song. "BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/509.

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Recent research has confirmed the feasibility of backdoor attacks in deep reinforcement learning (RL) systems. However, the existing attacks require the ability to arbitrarily modify an agent's observation, constraining the application scope to simple RL systems such as Atari games. In this paper, we migrate backdoor attacks to more complex RL systems involving multiple agents and explore the possibility of triggering the backdoor without directly manipulating the agent's observation. As a proof of concept, we demonstrate that an adversary agent can trigger the backdoor of the victim agent wit
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Yang, Hsuan-Kung, Po-Han Chiang, Min-Fong Hong, and Chun-Yi Lee. "Flow-based Intrinsic Curiosity Module." 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.2020/286.

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In this paper, we focus on a prediction-based novelty estimation strategy upon the deep reinforcement learning (DRL) framework, and present a flow-based intrinsic curiosity module (FICM) to exploit the prediction errors from optical flow estimation as exploration bonuses. We propose the concept of leveraging motion features captured between consecutive observations to evaluate the novelty of observations in an environment. FICM encourages a DRL agent to explore observations with unfamiliar motion features, and requires only two consecutive frames to obtain sufficient information when estimatin
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Lin, Yen-Chen, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, and Min Sun. "Tactics of Adversarial Attack on Deep Reinforcement Learning Agents." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/525.

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We introduce two tactics, namely the strategically-timed attack and the enchanting attack, to attack reinforcement learning agents trained by deep reinforcement learning algorithms using adversarial examples. In the strategically-timed attack, the adversary aims at minimizing the agent's reward by only attacking the agent at a small subset of time steps in an episode. Limiting the attack activity to this subset helps prevent detection of the attack by the agent. We propose a novel method to determine when an adversarial example should be crafted and applied. In the enchanting attack, the adver
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