prof.dr.ir. Pieter Spronck

Bio

Pieter Spronck studied computer science at Delft University of Technology, and received a PhD in Artificial Intelligence from Maastricht University. He held jobs as a programmer, systems designer, and knowledge engineer, before entering academics. He has worked for Tilburg University since 2008. Since 2016 he is a full professor of computer science, and since 2018 he heads the Department of Cognitive Science and Artificial Intelligence of the Tilburg School of Humanities and Digital Sciences. He teaches programming and artificial intelligence. His research interests include evolutionary systems, adaptive control, computer game AI, player modeling, multi-agent systems, knowledge technology, and serious games. These interests are grounded in the fields of computer science, artificial intelligence, and data science.

Expertise

Computer science, programming, artificial intelligence, machine learning, game research

Teaching

At Tilburg University, I teach the course "Computer Games" in the CS&AI master. In the CS&AI bachelor, I teach the course "Data Structures and Algorithms". Formerly, I taught the courses "Data Processing" and "Data Processing Advanced" in the HAIT/Data Science master, "Games and Social Simulations" in the research master, and "Understanding Intelligence" and "Games for Artificial Intelligence" (together with Sander Bakkes) in the CIS bachelor. I also contributed to the courses "Business Information Technology", "Digital Media Research Tools", and "Inleiding HAIT" in the CIS bachelor. I supervise many bachelor, master, and PhD students. At the Open University I taught several courses in Artificial Intelligence, and designed and taught a course focusing on computer games. At Maastricht University, I taught courses on Logic, Object-oriented design and programming, and computer games.

Courses

• Computer Games
• Programming and Algorithmic Thinking
• Programming and Algorithmic Thinking
• Thesis/Data Science in Action
• Data Structures and Algorithms

Recent publications

1. Predicting Tetris Performance Using Early Keystrokes

   Guglielmo, G., Klincewicz, M., Veld, E. H. I. ., & Spronck, P. (2023). Predicting Tetris Performance Using Early Keystrokes. In P. Lopes, F. Luz, A. Liapis, & H. Engstrom (Eds.), Proceedings of the 18th International Conference on the Foundations of Digital Games, FDG 2023 (pp. 1-4). [46] (ACM International Conference Proceeding Series). ACM.

2. Multiplayer Tension In the Wild: A Hearthstone Case

   Mavromoustakos Blom, P., Melhart, D., Liapis, A., Yannakakis, G., Bakkes, S., & Spronck, P. (2023). Multiplayer Tension In the Wild: A Hearthstone Case. 1-9. Paper presented at Foundations of Digital Games.

3. A Systematic Analysis of Vocabulary and BPE Settings for Optimal Fine…

   Sharami, J. P. R., Shterionov, D., & Spronck, P. (2023). A Systematic Analysis of Vocabulary and BPE Settings for Optimal Fine-tuning of NMT: A Case Study of In-domain Translation. Manuscript submitted for publication.

4. Tailoring Domain Adaptation for Machine Translation Quality Estimation

   Sharami, J. P. R., Shterionov, D., Blain, F., Vanmassenhove, E., Sisto, M. D., Emmery, C., & Spronck, P. (Accepted/In press). Tailoring Domain Adaptation for Machine Translation Quality Estimation. In Proceedings of the 24th Annual Conference of the European Association for Machine Translation

5. The simulator sickness questionnaire, and the erroneous zero baseline…

   Brown, P., Spronck, P., & Powell, W. (2022). The simulator sickness questionnaire, and the erroneous zero baseline assumption. Frontiers in Virtual Reality, 3, 1-14. [945800].