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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:

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

Showing 1-25 of 33 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
Dynamic difficulty adjustment approaches in video games: a systematic literature review
Fatemeh Mortazavi, Hadi Moradi, Abdol-Hossein Vahabie
Multimedia Tools and Applications (2024) Vol. 83, Iss. 35, pp. 83227-83274
Closed Access | Times Cited: 9
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
Child Safety and Protection in the Online Gaming Ecosystem
Anum Faraz, Jinane Mounsef, Ali Raza, et al.
IEEE Access (2022) Vol. 10, pp. 115895-115913
Open Access | Times Cited: 9
Applying Hybrid Reward Architecture to a Fighting Game AI
Yoshina Takano, Wenwen Ouyang, Suguru Ito, et al.
(2018), pp. 1-4
Closed Access | Times Cited: 13
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
$$\delta $$-logit : Dynamic Difficulty Adjustment Using Few Data Points
William Rao Fernandes, Guillaume Levieux
Lecture notes in computer science (2019), pp. 158-171
Open Access | Times Cited: 5
Motion Gaming AI using Time Series Forecasting and Dynamic Difficulty Adjustment
Takahiro Kusano, Yunshi Liu, Pujana Paliyawan, et al.
2021 IEEE Conference on Games (CoG) (2019)
Closed 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
Towards Social Facilitation in Audience Participation Games: Fighting Game AIs whose Strength Depends on Audience Responses
Pujana Paliyawan, Kingkarn Sookhanaphibarn, Worawat Choensawat, et al.
2021 IEEE Conference on Games (CoG) (2020), pp. 686-689
Closed Access | Times Cited: 4
Balancing the Performance of a FightingICE Agent using Reinforcement Learning and Skilled Experience Catalogue
Akash Cherukuri, Frank G. Glavin
(2022)
Closed Access | Times Cited: 3
Meta Artificial Intelligence and Artificial Intelligence Director
Daiki Satoi, Yuta Mizuno
Encyclopedia of Computer Graphics and Games (2024), pp. 1119-1126
Closed 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
Balanceamento em Estéticas e Dinâmicas Para Imersão em Certos Gêneros de Jogos - Revisão Rápida da Literatura
Herick Henrique Cardouzo, Tadeu Moreira de Classe
(2023), pp. 200-212
Open Access | Times Cited: 1
AlphaDDA: strategies for adjusting the playing strength of a fully trained AlphaZero system to a suitable human training partner
Kazuhisa Fujita
PeerJ Computer Science (2022) Vol. 8, pp. e1123-e1123
Open Access | Times Cited: 2
Difficulty Pacing Impact on Player Motivation
William Rao Fernandes, Guillaume Levieux
Lecture notes in computer science (2022), pp. 140-153
Closed Access | Times Cited: 2
Creating Adjustable Human-like AI Behavior in a 3D Tennis Game with Monte-Carlo Tree Search
Kaito Kimura, Yuan Tu, Riku Tanji, et al.
2021 IEEE Symposium Series on Computational Intelligence (SSCI) (2021)
Closed Access | Times Cited: 2
Meta Artificial Intelligence and Artificial Intelligence Director
Daiki Satoi, Yuta Mizuno
Encyclopedia of Computer Graphics and Games (2019), pp. 1-8
Closed Access | Times Cited: 1
Audience Participation Fighting Game: making an APG using a concept of social facilitation
Pujana Paliyawan, Ruck Thawonmas, Kingkarn Sookhanaphibarn, et al.
(2022)
Open Access | Times Cited: 1
Audience Participation Fighting Game: making an APG using a concept of social facilitation
Pujana Paliyawan, Ruck Thawonmas, Kingkarn Sookhanaphibarn, et al.
(2022)
Open Access | Times Cited: 1
Identification of Play Styles in Universal Fighting Engine
Kaori Yuda, S. Kamei, Riku Tanji, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 1
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