Cooperative AI in open-ended environments

I am interested in training agents capable of effective cooperation in complex, open-ended environments.

I am a Ph.D. candidate and research associate in the Collaborative Artificial Intelligence (CAI) group at the University of Stuttgart, advised by Andreas Bulling, and a scholar of the International Max Planck Research School for Intelligent Systems (IMPRS-IS).

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.

Google Scholar Group profile About me

Research

Generalisation and open-ended learning

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.

Coordination with unfamiliar partners

How can independently trained agents to cooperate? I study zero-shot coordination through entropy regularisation and grounded joint attention.

Social reasoning and language models

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.

News

Two papers accepted at EMNLP 2025

I am very happy to announce that we have two papers accepted at EMNLP 2025! Both works deal with Theory of Mind and Large Language Models. In the first we study how LLMs represent beliefs (findings) and in the second we propose a new benchmark...

Paper Accepted at TMLR

Our new work on evaluating agents’ ability to cooperate with new partners in unknown environments has been accepted at TMLR. The paper is titled The Overcooked Generalisation Challenge: Evaluating Cooperation with Novel Partners in Unknown Environments Using Unsupervised Environment Design.

Paper Accepted at ToM IJCAI 2025

I am very happy to announce that we have a paper accepted at ToM IJCAI 2025. Our work will be featured as a long oral talk at the workshop. The paper is titled The Yōkai Learning Environment: Tracking Beliefs Over Space and Time and proposes...

Attended the CoGenAI Summer School

I had a blast attending the ELLIS CoGenAI Summer School last week where I talked about our recent preprint “The Overcooked Generalisation Challenge” available here. For more details you can also see my LinkedIn post

Publications

See Google Scholar for citation information. An asterisk (*) denotes equal contribution.

Preprints

  1. 2026: Benintendi, G.*, Ruhdorfer, C.*, Kögel, F. & Bulling, A. Grounded Joint-Attention Other-Play for Zero-Shot Coordination. Preprint. [arXiv]

Conference and journal papers

  1. 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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

  1. 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

  1. 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]