Topic · Artificial Intelligence in Games
Most cited papers in Artificial Intelligence in Games
What are the most cited papers in Artificial Intelligence in Games?
The most cited paper in Artificial Intelligence in Games is "Mastering the game of Go with deep neural networks and tree search" (David Lawrence Silver, Aja Huang, Chris J. Maddison et al., 2016), with 16,121 citations. Next come "Mastering the game of Go without human knowledge" (9,370) and "From game design elements to gamefulness" (8,256).
Showing 50 of 100 papers. Every paper with more than 435 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Mastering the game of Go with deep neural networks and tree search David Lawrence Silver, Aja Huang, Chris J. Maddison et al. · Nature | 26 Jan 2016 | 16,121 |
| 2 | Mastering the game of Go without human knowledge David Lawrence Silver, Julian Schrittwieser, Karen Simonyan et al. · Nature | Oct 2017 | 9,370 |
| 3 | From game design elements to gamefulness Sebastian Deterding, Dan Dixon, Rilla Khaled et al. | 28 Sep 2011 | 8,256 |
| 4 | A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play David Lawrence Silver, Thomas Hubert, Julian Schrittwieser et al. · Science | 6 Dec 2018 | 3,636 |
| 5 | Grandmaster level in StarCraft II using multi-agent reinforcement learning Oriol Vinyals et al. · Nature | 30 Oct 2019 | 3,528 |
| 6 | A Survey of Monte Carlo Tree Search Methods Cameron Browne, Edward J. Powley, Daniel Whitehouse et al. · IEEE Transactions on Computational Intelligence and AI in Games | 3 Feb 2012 | 3,032 |
| 7 | Learning to predict by the methods of temporal differences Richard S. Sutton · Machine Learning | Aug 1988 | 2,826 |
| 8 | Markov games as a framework for multi-agent reinforcement learning Michael L. Littman · Elsevier eBooks | 1994 | 2,722 |
| 9 | New Ideas In Optimization David Corne, Marco Dorigo, Fred Glover et al. | Oct 1999 | 2,133 |
| 10 | Generative Agents: Interactive Simulacra of Human Behavior Joon Sung Park, Joseph O’Brien, Carrie Jun Cai et al. | 20 Oct 2023 | 1,861 |
| 11 | Depth-first iterative-deepening Richard E. Korf · Artificial Intelligence | Sep 1985 | 1,566 |
| 12 | Temporal difference learning and TD-Gammon Gerald Tesauro · Communications of the ACM | Mar 1995 | 1,496 |
| 13 | Generalization as search Tom M. Mitchell · Artificial Intelligence | Mar 1982 | 1,444 |
| 14 | Predicting protein structures with a multiplayer online game Seth Cooper, Firas Khatib, Adrien Treuille et al. · Nature | Aug 2010 | 1,406 |
| 15 | Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search Rémi Coulom · Lecture notes in computer science | 2007 | 1,279 |
| 16 | Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents Mitchell A. Potter, Kenneth De Jong · Evolutionary Computation | Mar 2000 | 1,208 |
| 17 | Games and Information: An Introduction to Game Theory Eric Rasmusen | 28 Nov 2006 | 1,195 |
| 18 | Designing games with a purpose Luis von Ahn, Laura Dabbish · Communications of the ACM | 29 Jul 2008 | 1,169 |
| 19 | Mastering Atari, Go, chess and shogi by planning with a learned model Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert et al. · Nature | 23 Dec 2020 | 1,167 |
| 20 | A grounded investigation of game immersion Emily Brown, Paul A. Cairns | 24 Apr 2004 | 1,153 |
| 21 | Analysis of Recursive Game Graphs Using Data Flow Equations Kousha Etessami · Lecture notes in computer science | 2004 | 1,136 |
| 22 | Learning Efficient Classification Procedures and Their Application to Chess End Games J. R. Quinlan · Machine Learning | 1983 | 1,107 |
| 23 | The Arcade Learning Environment: An Evaluation Platform for General Agents M. G. Bellemare, Y. Naddaf, J. Veness et al. · Journal of Artificial Intelligence Research | 14 Jun 2013 | 1,086 |
| 24 | The logic of inexact concepts Joseph A. Goguen · Synthese | Apr 1969 | 1,015 |
| 25 | An analysis of alpha-beta pruning Donald E. Knuth, Ronald W. Moore · Artificial Intelligence | 1975 | 1,005 |
| 26 | The Scientific Research Potential of Virtual Worlds William Sims Bainbridge · Science | 26 Jul 2007 | 945 |
| 27 | Real-time heuristic search Richard E. Korf · Artificial Intelligence | Mar 1990 | 921 |
| 28 | Empirical Studies of Programming Knowledge Elliot M. Soloway, Kate Ehrlich · IEEE Transactions on Software Engineering | Sep 1984 | 880 |
| 29 | Monte-Carlo Planning in Large POMDPs David Lawrence Silver, Joel Veness · DSpace@MIT (Massachusetts Institute of Technology) | 6 Dec 2010 | 852 |
| 30 | DeepMimic Xue Bin Peng, Pieter Abbeel, Sergey Levine et al. · ACM Transactions on Graphics | 30 Jul 2018 | 851 |
| 31 | Mapping learning and game mechanics for serious games analysis Sylvester Arnab, Theodore Lim, Maira B. Carvalho et al. · British Journal of Educational Technology | 5 Jan 2014 | 849 |
| 32 | DeepStack: Expert-level artificial intelligence in heads-up no-limit poker Matej Moravčík, Martin Schmid, NEIL R. BURCH et al. · Science | 2 Mar 2017 | 813 |
| 33 | TD-Gammon, a Self-Teaching Backgammon Program, Achieves Master-Level Play Gerald J. Tesauro · Neural Computation | Mar 1994 | 804 |
| 34 | Practical issues in temporal difference learning Gerald J. Tesauro · Machine Learning | May 1992 | 800 |
| 35 | Parametric shape analysis via 3-valued logic Mooly Sagiv, Thomas Reps, Reinhard Wilhelm · ACM Transactions on Programming Languages and Systems | May 2002 | 786 |
| 36 | Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010) Jürgen Schmidhuber · IEEE Transactions on Autonomous Mental Development | 21 Jul 2010 | 779 |
| 37 | Compressed full-text indexes Gonzalo Navarro, Veli Mäkinen · ACM Computing Surveys | 9 Apr 2007 | 772 |
| 38 | Temporal Difference Learning and TD-Gammon G. Tesauro · ICGA Journal | Jun 1995 | 768 |
| 39 | Toward Efficient Trajectory Planning: The Path-Velocity Decomposition Kamal Kant, Steven W. Zucker · The International Journal of Robotics Research | Sep 1986 | 759 |
| 40 | Machine Learning from Theory to Algorithms: An Overview Jafar Ahmad Abed Alzubi, Anand Nayyar, Akshi Kumar · Journal of Physics Conference Series | Nov 2018 | 746 |
| 41 | Why Functional Programming Matters John Hughes · The Computer Journal | Feb 1989 | 743 |
| 42 | BEAT Justine Cassell, Hannes Högni Vilhjálmsson, Timothy Bickmore | Aug 2001 | 740 |
| 43 | Artificial intelligence in the creative industries: a review Nantheera Anantrasirichai, David Bull · Artificial Intelligence Review | Jul 2021 | 735 |
| 44 | Generative AI enhances individual creativity but reduces the collective diversity of novel content Anil Rajnikant Doshi, Oliver Hauser · Science Advances | 12 Jul 2024 | 730 |
| 45 | Search-Based Procedural Content Generation: A Taxonomy and Survey Julian Togelius, Georgios N. Yannakakis, Kenneth Owen Stanley et al. · IEEE Transactions on Computational Intelligence and AI in Games | 3 May 2011 | 711 |
| 46 | Some Studies in Machine Learning Using the Game of Checkers. II—Recent Progress Arthur L. Samuel · IBM Journal of Research and Development | Nov 1967 | 699 |
| 47 | Human-level performance in 3D multiplayer games with population-based reinforcement learning Max Jaderberg, Wojciech Marian Czarnecki, Iain Dunning et al. · Science | 30 May 2019 | 695 |
| 48 | Cooperative Pathfinding David Lawrence Silver · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment | Jun 2005 | 693 |
| 49 | Memetic algorithms and memetic computing optimization: A literature review Ferrante Neri, Carlos Cotta · Swarm and Evolutionary Computation | 9 Dec 2011 | 684 |
| 50 | Unifying count-based exploration and intrinsic motivation Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski et al. · Neural Information Processing Systems | 5 Dec 2016 | 670 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.