My research spans multi-agent reinforcement learning, ad-hoc teamwork, world models, and computational Theory of Mind. I study how agents can generalise to unfamiliar teammates and environments, coordinate without training together, and reason about others' beliefs.
How can we train agents to cooperate beyond the partners and tasks they have seen? I develop methods and benchmarks for partner and environment generalisation, including Unsupervised Partner Design and The Overcooked Generalisation Challenge. My recent work also explores multi-agent world models in DreamTeam and continual multi-agent learning in MEAL.
I investigate whether language models can reason about other agents' beliefs and how they represent those beliefs internally, through work such as ToM-SSI and Brittle Minds, Fixable Activations. I am also exploring open-ended and population-based reinforcement learning for safer LLM post-training.
Happy to announce that we have a paper accepted at the European Conference on Artificial Intelligence (ECAI). Our work deals with the modelling of mental states of people in nonverbal social interactions and specifically builds on top of the BOSS and TBD dataset. Bortoletto, M.,...
We have a paper accepted at the Workshop on Mechanistic Interpretability located at ICML this year! Bortoletto, M., Ruhdorfer, C., Shi, L. & Bulling, A. (2024). Benchmarking Mental State Representations in Language Models. ICML Workshop on Mechanistic Interpretability. Thanks to my co-authors and especially the...
I am very happy to announce that we have a paper accepted at ACL 2024 in the main conference. The paper is titled Limits of Theory of Mind Modelling in Dialogue-Based Collaborative Plan Acquisition and explores a recent issue in Theory of Mind modelling in...
Happy to announce that I will attend the ELLIS summer school on Collaborative and Generative AI (CoGenAI) 2024 at Aalto University in Finland.
For details please refer to the official website here.
Matteo Bortoletto, Anna Penzkofer and I are organizing a practical course on Computational Theory of Mind and Cognition for master students in computing fields at the University of Stuttgart. The goal of this course is to familiarise students with exciting current research topics at the...
Publications
See Google Scholar for citation information. An asterisk (*) denotes equal contribution.
Preprints
2026: Benintendi, G.*, Ruhdorfer, C.*, Kögel, F. & Bulling, A. Grounded Joint-Attention Other-Play for Zero-Shot Coordination. Preprint. [arXiv]
Conference and journal papers
2026: Forkel, J., Ruhdorfer, C., Beukman, M., Bulling, A. & Foerster, J. N. High Entropy Regularization Leads to Symmetry-Equivariant Policies in Dec-POMDPs. NeurIPS 2026. [website][arXiv]
2026:Ruhdorfer, C., Bortoletto, M., Forkel, J., Foerster, J. N. & Bulling, A. The Yōkai Learning Environment: Tracking Beliefs Over Space and Time. Reinforcement Learning Conference (RLC). [website][arXiv]
2026:Ruhdorfer, C., Bortoletto, M., Oei, V., Penzkofer, A. & Bulling, A. Unsupervised Partner Design Enables Robust Ad-hoc Teamwork. International Conference on Machine Learning (ICML). Oral[website][arXiv]
2026: Tomilin, T., van den Boogaard, L., Garcin, S., Ruhdorfer, C., Grooten, B., Kusters, F., Du, Y., Bulling, A., Pechenizkiy, M. & Fang, M. MEAL: A Benchmark for Continual Multi-Agent Reinforcement Learning. International Conference on Machine Learning (ICML). [paper]
2025: Bortoletto, M., Ruhdorfer, C. & Bulling, A. (2025). ToM-SSI: Evaluating Theory of Mind in Situated Social Interactions. Proc. Empirical Methods in Natural Language Processing (EMNLP) [website][arXiv]
2025: Bortoletto, M., Ruhdorfer, C., Shi, L. & Bulling, A. (2025). Brittle Minds, Fixable Activations: Understanding Belief Representations in Language Models. Findings of Empirical Methods in Natural Language Processing (EMNLP Findings) [website][arXiv]
2025:Ruhdorfer, C., Bortoletto, M., Penzkofer, A. & Bulling, A. (2025). The Overcooked Generalisation Challenge: Evaluating Cooperation with Novel Partners in Unknown Environments Using Unsupervised Environment Design. Transactions on Machine Learning Research. [paper][website][arXiv]
2024: Bortoletto, M., Ruhdorfer, C., Shi, L. & Bulling, A. (2024). Explicit Modelling of Theory of Mind for Belief Prediction in Nonverbal Social Interactions. Proc. European Conference on Artificial Intelligence (ECAI), pp. 866–873, 2024. (oral)[paper][website][arXiv]
2024: Bortoletto, M., Ruhdorfer, C.*, Abdessaied, A.*, Shi, L. & Bulling, A. (2024). Limits of Theory of Mind Modelling in Dialogue-Based Collaborative Plan Acquisition. Proc. 62nd Annual Meeting of the Association for Computational Linguistics (ACL), pp. 1–16, 2024. [paper][website][arXiv][acl]
2024: Wang, Y., Jiang, Y., Hu, Z., Ruhdorfer, C., Bâce, M., & Bulling, A. (2024). VisRecall++: Analysing and Predicting Visualisation Recallability from Gaze Behaviour. Proc. ACM on Human-Computer Interaction (PACM HCI), 8(ETRA), 1–18. [paper][website][acm]
Workshop papers
2020:Ruhdorfer, C., & Schulz, S. (2020). Efficient Implementation of Large-Scale Watchlists. PAAR+SC2 @ International Joint Conference on Automated Reasoning (IJCAR) (pp. 120-133). [paper][website]
Other collaborations
2025: Hofmeyer, P. E., Burghaus, H., Ruhdorfer, C., Oswald, J., Bulling, A., Herdrich, G. (2025) Integration of Machine Learning in High-Enthalpy Plasma Spectroscopy. Proc. International Conference on Flight vehicles, Aerothermodynamics and Re-entry (FAR), pp. 1–8 [paper][website]