Matthew Guzdial

731 total citations
38 papers, 172 citations indexed

About

Matthew Guzdial is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Sociology and Political Science. According to data from OpenAlex, Matthew Guzdial has authored 38 papers receiving a total of 172 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Artificial Intelligence, 17 papers in Computer Vision and Pattern Recognition and 12 papers in Sociology and Political Science. Recurrent topics in Matthew Guzdial's work include Artificial Intelligence in Games (30 papers), Digital Games and Media (12 papers) and Video Analysis and Summarization (12 papers). Matthew Guzdial is often cited by papers focused on Artificial Intelligence in Games (30 papers), Digital Games and Media (12 papers) and Video Analysis and Summarization (12 papers). Matthew Guzdial collaborates with scholars based in Canada, United States and United Kingdom. Matthew Guzdial's co-authors include Mark Riedl, Boyang Li, Nathan Sturtevant, Adam Summerville, Sam Snodgrass, Brent Harrison, Mrunal Jadhav, Jonathan H. Chen, Shaoyu Chen and Seth Cooper and has published in prestigious journals such as Neural Computing and Applications, IEEE Transactions on Games and arXiv (Cornell University).

In The Last Decade

Matthew Guzdial

28 papers receiving 157 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Matthew Guzdial Canada 7 132 80 68 32 24 38 172
Adam Summerville United States 11 234 1.8× 110 1.4× 136 2.0× 50 1.6× 27 1.1× 29 273
Chong-U Lim United States 7 157 1.2× 48 0.6× 111 1.6× 34 1.1× 15 0.6× 18 204
Tommy Thompson United Kingdom 6 228 1.7× 55 0.7× 131 1.9× 45 1.4× 16 0.7× 20 258
Philip Bontrager United States 6 95 0.7× 81 1.0× 44 0.6× 14 0.4× 9 0.4× 9 172
Matthew Stephenson Australia 8 108 0.8× 54 0.7× 55 0.8× 20 0.6× 20 0.8× 47 168
Adrien Couëtoux United Kingdom 3 120 0.9× 29 0.4× 62 0.9× 15 0.5× 10 0.4× 4 129
Angelo E. M. Ciarlini Brazil 7 92 0.7× 51 0.6× 50 0.7× 12 0.4× 37 1.5× 27 153
Steve Dahlskog Sweden 12 273 2.1× 150 1.9× 178 2.6× 75 2.3× 50 2.1× 15 311
Raluca D. Gaina United Kingdom 7 103 0.8× 24 0.3× 58 0.9× 20 0.6× 5 0.2× 19 124
Ralph Gasser Switzerland 11 113 0.9× 247 3.1× 18 0.3× 13 0.4× 4 0.2× 21 303

Countries citing papers authored by Matthew Guzdial

Since Specialization
Citations

This map shows the geographic impact of Matthew Guzdial's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Matthew Guzdial with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew Guzdial more than expected).

Fields of papers citing papers by Matthew Guzdial

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matthew Guzdial. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Matthew Guzdial. The network helps show where Matthew Guzdial may publish in the future.

Co-authorship network of co-authors of Matthew Guzdial

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew Guzdial. A scholar is included among the top collaborators of Matthew Guzdial based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Matthew Guzdial. Matthew Guzdial is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Islam, Md. Saiful, et al.. (2025). Human-AI collaboration in real-world complex environment with reinforcement learning. Neural Computing and Applications. 37(23). 18957–18987.
2.
Guzdial, Matthew, et al.. (2024). The FarmQuest Player Telemetry Dataset: Playthrough Data of a Cozy Farming Game. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 20(1). 263–268.
3.
Cooper, Seth, et al.. (2023). Mechanic Maker 2.0: Reinforcement Learning for Evaluating Generated Rules. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 19(1). 266–275. 4 indexed citations
4.
Guzdial, Matthew, et al.. (2023). Reconstructing Existing Levels through Level Inpainting. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 19(1). 276–283.
5.
Cooper, Seth, et al.. (2023). Game Level Blending using a Learned Level Representation. 1–8.
6.
Guzdial, Matthew, et al.. (2023). Joint Level Generation and Translation Using Gameplay Videos. 1–10. 1 indexed citations
7.
Guzdial, Matthew, et al.. (2022). World Models with an Entity-Based Representation. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 18(1). 215–222. 1 indexed citations
8.
Guzdial, Matthew, et al.. (2021). The Impact of Visualizing Design Gradients for Human Designers. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 17(1). 18–25.
9.
Sturtevant, Nathan, et al.. (2021). Towards Disambiguating Quests as a Technical Term. 1–11. 2 indexed citations
10.
Guzdial, Matthew, et al.. (2021). Arachnophobia Exposure Therapy Using Experience-Driven Procedural Content Generation via Reinforcement Learning (EDPCGRL). Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 17(1). 164–171. 3 indexed citations
11.
Guzdial, Matthew & Mark Riedl. (2021). Conceptual Game Expansion. IEEE Transactions on Games. 14(1). 93–106. 4 indexed citations
12.
Jadhav, Mrunal & Matthew Guzdial. (2021). Tile Embedding: A General Representation for Level Generation. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 17(1). 34–41. 6 indexed citations
13.
Guzdial, Matthew & Mark Riedl. (2018). Combinatorial Creativity for Procedural Content Generation via Machine Learning.. National Conference on Artificial Intelligence. 557–564. 5 indexed citations
14.
Guzdial, Matthew, et al.. (2018). Co-Creative Level Design via Machine Learning. arXiv (Cornell University). 1 indexed citations
15.
Guzdial, Matthew & Mark Riedl. (2018). Automated Game Design via Conceptual Expansion. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 14(1). 31–37. 10 indexed citations
16.
Guzdial, Matthew, et al.. (2018). Player Experience Extraction from Gameplay Video. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 14(1). 52–58. 6 indexed citations
17.
Guzdial, Matthew, Jonathan H. Chen, Shaoyu Chen, & Mark Riedl. (2017). A General Level Design Editor for Co-Creative Level Design. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 13(2). 81–83. 5 indexed citations
18.
Guzdial, Matthew & Mark Riedl. (2016). Game Level Generation from Gameplay Videos. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 12(1). 44–50. 40 indexed citations
19.
Guzdial, Matthew, Nathan Sturtevant, & Boyang Li. (2016). Deep Static and Dynamic Level Analysis: A Study on Infinite Mario. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 12(2). 31–38. 8 indexed citations
20.
Guzdial, Matthew, Brent Harrison, Boyang Li, & Mark Riedl. (2015). Crowdsourcing Open Interactive Narrative.. Foundations of Digital Games. 13 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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