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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:

Opponent modeling based on action table for MCTS-based fighting game AI
Man-Je Kim, Kyung-Joong Kim
(2017), pp. 178-180
Closed Access | Times Cited: 28

Showing 1-25 of 28 citing articles:

Monte Carlo Tree Search: a review of recent modifications and applications
Maciej Świechowski, Konrad Godlewski, Bartosz Sawicki, et al.
Artificial Intelligence Review (2022) Vol. 56, Iss. 3, pp. 2497-2562
Open Access | Times Cited: 168
Creating Pro-Level AI for a Real-Time Fighting Game Using Deep Reinforcement Learning
In-Seok Oh, Seungeun Rho, Sangbin Moon, et al.
IEEE Transactions on Games (2021) Vol. 14, Iss. 2, pp. 212-220
Open Access | Times Cited: 58
AI in Human-computer Gaming: Techniques, Challenges and Opportunities
Qiyue Yin, Jun Yang, Kaiqi Huang, et al.
Deleted Journal (2023) Vol. 20, Iss. 3, pp. 299-317
Open Access | Times Cited: 17
A Survey of Opponent Modeling in Adversarial Domains
Samer B. Nashed, Shlomo Zilberstein
Journal of Artificial Intelligence Research (2022) Vol. 73, pp. 277-327
Open Access | Times Cited: 22
Monte-Carlo Tree Search for Implementation of Dynamic Difficulty Adjustment Fighting Game AIs Having Believable Behaviors
Makoto Ishihara, Suguru Ito, Ryota Ishii, et al.
(2018), pp. 1-8
Closed Access | Times Cited: 33
Hierarchical Reinforcement Learning With Monte Carlo Tree Search in Computer Fighting Game
Ivan Pereira Pinto, Luciano Coutinho
IEEE Transactions on Games (2018) Vol. 11, Iss. 3, pp. 290-295
Closed Access | Times Cited: 25
Enhanced Rolling Horizon Evolution Algorithm With Opponent Model Learning: Results for the Fighting Game AI Competition
Zhentao Tang, Yuanheng Zhu, Dongbin Zhao, et al.
IEEE Transactions on Games (2020) Vol. 15, Iss. 1, pp. 5-15
Open Access | Times Cited: 18
Evolving population method for real-time reinforcement learning
Man-Je Kim, Jun Suk Kim, Chang Wook Ahn
Expert Systems with Applications (2023) Vol. 229, pp. 120493-120493
Closed Access | Times Cited: 6
Monte-Carlo Tree Search Implementation of Fighting Game AIs Having Personas
Ryota Ishii, Suguru Ito, Makoto Ishihara, et al.
(2018), pp. 1-8
Closed Access | Times Cited: 19
Hybrid fighting game AI using a genetic algorithm and Monte Carlo tree search
Man-Je Kim, Chang Wook Ahn
Proceedings of the Genetic and Evolutionary Computation Conference Companion (2018), pp. 129-130
Closed Access | Times Cited: 16
A Fighting Game AI Using Highlight Cues for Generation of Entertaining Gameplay
Ryota Ishii, Suguru Ito, Ruck Thawonmas, et al.
2021 IEEE Conference on Games (CoG) (2019)
Closed Access | Times Cited: 10
Application of Retrograde Analysis on Fighting Games
Kristen Yu, Nathan Sturtevant
2021 IEEE Conference on Games (CoG) (2019), pp. 1-8
Closed Access | Times Cited: 8
Monte Carlo Tree Search: A Review of Recent Modifications and Applications.
Maciej Świechowski, Konrad Godlewski, Bartosz Sawicki, et al.
arXiv (Cornell University) (2021)
Closed Access | Times Cited: 6
Creating Pro-Level AI for a Real-Time Fighting Game Using Deep Reinforcement Learning
In-Seok Oh, Seungeun Rho, Sangbin Moon, et al.
arXiv (Cornell University) (2019)
Closed Access | Times Cited: 5
Coping with opponents: multi-objective evolutionary neural networks for fighting games
Steven Künzel, Silja Meyer-Nieberg
Neural Computing and Applications (2020) Vol. 32, Iss. 17, pp. 13885-13916
Open Access | Times Cited: 4
A novel real-time design for fighting game AI
Gia Thuan Lam, Doina Logofătu, Costin Bădică
Evolving Systems (2020) Vol. 12, Iss. 1, pp. 169-176
Closed Access | Times Cited: 4
Designing an Automatic Agent for Repeated Language–based Persuasion Games
Maya Raifer, Guy Rotman, Reut Apel, et al.
Transactions of the Association for Computational Linguistics (2022) Vol. 10, pp. 307-324
Open Access | Times Cited: 3
Enhanced Naive Agent in Angry Birds AI Competition via Exploitation-Oriented Learning
Kazuteru Miyazaki
Journal of Robotics and Mechatronics (2024) Vol. 36, Iss. 3, pp. 580-588
Open Access
Fighting-Game Gameplay Generation Using Highlight Cues
Ryota Ishii, Keita Fujimaki, Ruck Thawonmas
IEEE Transactions on Games (2021) Vol. 14, Iss. 3, pp. 533-538
Closed Access | Times Cited: 4
Role of AI in Gaming and Simulation
Shivam Tyagi, Sudhriti Sengupta
Lecture notes on data engineering and communications technologies (2020), pp. 259-266
Closed 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
Interpretable Utility-based Models Applied to the FightingICE Platform
Tianyu Chen, Florian Richoux, Javier Martínez Martínez, et al.
2021 IEEE Conference on Games (CoG) (2021), pp. 1-8
Open Access | Times Cited: 3
OM-TCN: A dynamic and agile opponent modeling approach for competitive games
Yuxi Ma, Meng Shen, Nan Zhang, et al.
Information Sciences (2022) Vol. 615, pp. 405-414
Closed Access | Times Cited: 2
Genetic Action Sequence for Integration of Agent Actions
Man-Je Kim, Jun Suk Kim, Donghyeon Lee, et al.
Communications in computer and information science (2020), pp. 682-688
Closed Access | Times Cited: 1
Improving Agent Decision Payoffs via a New Framework of Opponent Modeling
Chanjuan Liu, Jinmiao Cong, Tianhao Zhao, et al.
Mathematics (2023) Vol. 11, Iss. 14, pp. 3062-3062
Open Access
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