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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Re-evaluating Evaluation
David Balduzzi, Karl Tuyls, Julien Pérolat, et al.
arXiv (Cornell University) (2018)
Open Access | Times Cited: 30

Showing 1-25 of 30 citing articles:

AlphaStar
Kai Arulkumaran, Antoine Cully, Julian Togelius
Proceedings of the Genetic and Evolutionary Computation Conference Companion (2019), pp. 314-315
Open Access | Times Cited: 129
α-Rank: Multi-Agent Evaluation by Evolution
Shayegan Omidshafiei, Christos H. Papadimitriou, Georgios Piliouras, et al.
Scientific Reports (2019) Vol. 9, Iss. 1
Open Access | Times Cited: 59
Shared-endpoint correlations and hierarchy in random flows on graphs
Joshua Richland, Alexander Strang
Results in Applied Mathematics (2025) Vol. 26, pp. 100549-100549
Closed Access
Emergent Coordination Through Competition
Siqi Liu, Guy Lever, Josh Merel, et al.
arXiv (Cornell University) (2019)
Closed Access | Times Cited: 41
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence
Yuhang Song, Andrzej Wojcicki, Thomas Lukasiewicz, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2020) Vol. 34, Iss. 05, pp. 7253-7260
Open Access | Times Cited: 26
Diverse Agents for Ad-Hoc Cooperation in Hanabi
Rodrigo Canaan, Julian Togelius, Andy Nealen, et al.
2021 IEEE Conference on Games (CoG) (2019), pp. 1-8
Open Access | Times Cited: 25
Dual Indicators to Analyze AI Benchmarks: Difficulty, Discrimination, Ability, and Generality
Fernando Martínez‐Plumed, José Hernández‐Orallo
IEEE Transactions on Games (2018) Vol. 12, Iss. 2, pp. 121-131
Open Access | Times Cited: 20
Iterated Deep Reinforcement Learning in Games
Mason Wright, Yongzhao Wang, Michael P. Wellman
(2019), pp. 617-636
Open Access | Times Cited: 18
alpha-Rank: Multi-Agent Evaluation by Evolution
Shayegan Omidshafiei, Christos H. Papadimitriou, Georgios Piliouras, et al.
arXiv (Cornell University) (2019)
Closed Access | Times Cited: 14
Differentiable Game Mechanics
Alistair Letcher, David Balduzzi, Sébastien Racanière, et al.
arXiv (Cornell University) (2019)
Open Access | Times Cited: 13
Navigating the landscape of multiplayer games
Shayegan Omidshafiei, Karl Tuyls, Wojciech Marian Czarnecki, et al.
Nature Communications (2020) Vol. 11, Iss. 1
Open Access | Times Cited: 12
Continual Match Based Training in Pommerman: Technical Report
Peng Peng, Liang Pang, Yufeng Yuan, et al.
arXiv (Cornell University) (2018)
Open Access | Times Cited: 10
OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning
Alexander Sasha Vezhnevets, Yuhuai Wu, Maria K. Eckstein, et al.
International Conference on Machine Learning (2020) Vol. 1, pp. 9733-9742
Closed Access | Times Cited: 8
Scaling Scaling Laws with Board Games
Andrew Jones
arXiv (Cornell University) (2021)
Open Access | Times Cited: 8
The Benchmark Lottery.
Mostafa Dehghani, Yi Tay, Alexey A. Gritsenko, et al.
arXiv (Cornell University) (2021)
Closed Access | Times Cited: 8
Analysing Results from AI Benchmarks: Key Indicators and How to Obtain Them
Fernando Martínez‐Plumed, José Hernández‐Orallo
(2018)
Open Access | Times Cited: 8
Approximate Exploitability: Learning a Best Response
Finbarr Timbers, Nolan Bard, Edward Lockhart, et al.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (2022), pp. 3487-3493
Open Access | Times Cited: 4
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence
Yuhang Song, Andrzej Wojcicki, Thomas Lukasiewicz, et al.
arXiv (Cornell University) (2019)
Open Access | Times Cited: 5
Exploring Adversarial Artificial Intelligence for Autonomous Adaptive Cyber Defense
Erik Hemberg, Linda Zhang, Una-May O’Reilly
Springer eBooks (2020), pp. 41-61
Closed Access | Times Cited: 4
Intelligence via ultrafilters: structural properties of some intelligence comparators of deterministic Legg-Hutter agents
Samuel Allen Alexander
Journal of Artificial General Intelligence (2019) Vol. 10, Iss. 1, pp. 24-45
Open Access | Times Cited: 4
Learning to Identify Top Elo Ratings: A Dueling Bandits Approach
Xue Yan, Yali Du, Binxin Ru, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2022) Vol. 36, Iss. 8, pp. 8797-8805
Open Access | Times Cited: 3
Approximate exploitability: Learning a best response in large games.
Finbarr Timbers, Edward Lockhart, Martin Schmid, et al.
arXiv (Cornell University) (2020)
Closed Access | Times Cited: 3
Disturbance Decoupling for Gradient-Based Multi-Agent Learning With Quadratic Costs
Sarah H. Q. Li, Lillian J. Ratliff, Behçet Açıkmeşe
IEEE Control Systems Letters (2020) Vol. 5, Iss. 1, pp. 223-228
Open Access | Times Cited: 3
Estimating α-Rank by Maximizing Information Gain
Tabish Rashid, Cheng Zhang, Kamil Ciosek
Proceedings of the AAAI Conference on Artificial Intelligence (2021) Vol. 35, Iss. 6, pp. 5673-5681
Open Access | Times Cited: 3
AI in Games: Techniques, Challenges and Opportunities
Qiyue Yin, Jun Yang, Wancheng Ni, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 3
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