Preuss, Mike

Planning chemical syntheses with deep neural networks and symbolic AI

Segler, M. H. S., Preuss, M., & Waller, M. P. (2018) · Nature

Computational Drug Discovery MethodsAI-based Problem Solving and Planning

Cited by 1,847

A survey of real-time strategy game AI research and competition in StarCraft

Ontanon, S., Synnaeve, G., Uriarte, A., Richoux, F., Churchill, D., & Preuss, M. (2013) · IEEE Transactions on Computational Intelligence and AI in Games

Artificial Intelligence in GamesAI-based Problem Solving and Planning

Cited by 423

Agentic Large Language Models, a Survey

Plaat, A., Van Duijn, M., Van Stein, N., Preuss, M., Van der Putten, P., & Batenburg, K. J. (2025) · Journal of Artificial Intelligence Research

Topic ModelingAI-based Problem Solving and Planning

Cited by 75

Controllable procedural map generation via multiobjective evolution

Togelius, J., Preuss, M., Beume, N., Wessing, S., Hagelbäck, J., Yannakakis, G. N., & Grappiolo, C. (2013) · Genetic Programming and Evolvable Machines

Artificial Intelligence in GamesEvolutionary Algorithms and Applications

Cited by 48

New Automation for Social Bots: From Trivial Behavior to AI-Powered Communication

Grimme, C., Pohl, J., Cresci, S., Lüling, R., & Preuss, M. (2022) · Lecture Notes in Computer Science

Misinformation and Its ImpactsSpam and Phishing Detection

Cited by 19

Designing for Playfulness in Human-AI Authoring Tools

Liapis, A., Guckelsberger, C., Zhu, J., Harteveld, C., Kriglstein, S., Denisova, A., Gow, J., & Preuss, M. (2023) · Proceedings of the 18th International Conference on the Foundations of Digital Games

Innovative Human-Technology InteractionUsability and User Interface Design

Cited by 14

Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning

Muller-Brockhausen, M., Preuss, M., & Plaat, A. (2021) · 2021 IEEE Conference on Games (CoG)

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 11