Welcome!
I am a Ph.D. candidate and research associate in the
Collaborative Artificial Intelligence (CAI) group at the
University of Stuttgart, supervised by
Prof. Dr. Andreas Bulling.
I am also a Ph.D. scholar with the
International Max Planck Research School for Intelligent Systems (IMPRS-IS).
My IMPRS-IS Thesis Advisory Committee includes Prof. Dr. Georg Martius (
Max Planck Institute for Intelliegent Systems,
University of Tübingen) and Dr. Charley Wu (
University of Tübingen), alongside Prof. Bulling.
I research cooperative AI with a focus on social reasoning and generalisation.
Specifically, I work on multi-agent reinforcement learning (MARL), ad-hoc teamwork, large language models (LLMs), and Theory of Mind (ToM) in cooperative tasks.
My long-term goal is to train agents capable of ad-hoc teamwork with diverse partners in unknown, open-ended environments.
Among other things I research ...
- ... the intersection between partner and environment generalisation. In The Overcooked Generalisation Challenge we introduced a benchmark where agents must cooperate with novel partners in unknown environments.
In Unsupervised Partner Design (UPD), we proposed a method addressing this challenge.
- ... how computational Theory of Mind can be used to improve human-AI cooperation. In the Yokai Learning Environment we introduced a new zero-shot coordination benchmark for social reasoning, and in our work on LLMs we evaluated their Theory of Mind capabilities (e.g. 1, 2).
You can find more about me or my work on the
about page, on our
groups website, or on
google scholar.
For students interested in a project or thesis please check the
open thesis page.
Publications
Published
- 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]
- 2025: Ruhdorfer, C., Bortoletto, M. & Bulling, A. (2025) The Yōkai Learning Environment: Tracking Beliefs Over Space and Time. IJCAI Workshop on Generative AI & Theory of Mind In Communicating Agents [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. 1–7, 2024. (oral) [paper] [website] [arxiv]
- 2024: Bortoletto, M., Ruhdorfer, C., Shi, L. & Bulling, A. (2024). Benchmarking Mental State Representations in Language Models. ICML Workshop on Mechanistic Interpretability. [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., Bhâce, M., & Bulling, A. (2024). VisRecall++: Analysing and Predicting Visualisation Recallability from Gaze Bahaviour. Proc. ACM on Human-Computer Interaction (PACM HCI), 1–11. [paper] [website] [acm]
- 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]
I sometimes collaborate with others to work on topics not related to my core research interests:
- 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]
Preprints
- 2025: Ruhdorfer, C., Bortoletto, M., Oei, V., Penzkofer, A. & Bulling, A. (2025). Unsupervised Partner Design Enables Robust Ad-Hoc Teamwork. [arxiv]
Conferences, Workshops and Other Academic Meetings
Awards
- 2024: Rul Gunzenhäuser award for the best Master's thesis at the Institute for Visualisation and Interactive Systems, Stuttgart University in 2023.[announcement] [website]
Teaching
For details please refer to
here.
- 2025 (winter): Machine Perception and Learning (Tutor) (Master level lecture)
- 2025 (winter): Advanced Topics in Collaborative AI (Tutor) (Master level seminar)
- 2024 (winter): Mediainformatics (Organizer and Tutor) (Bachelor level course)
- 2024 (summer): Computational Theory of Mind and Cognition (Organizer, Tutor and Lecturer) (Master level practical course)
- 2024 (summer): Human Computer Interaction (Tutor) (Bachelor level lecture)
- 2023 (winter): Machine Perception and Learning (Tutor) (Master level lecture)
Supervision
I regulary supervise Master and Bachelor theses, see
here for a full list and if interested our
open projects page.
To this day I was or am involved in supervising a total of 3 students in their Master Thesis and a total of 8 students in various group projects offered through some of our lectures.
News
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., Ruhdorfer, C., Shi, L. & Bulling, A. (2024).
Explicit Modelling of Theory of Mind for Belief Prediction in Nonverbal Social Interactions
Proc. 27th European Conference on Artificial Intelligence (ECAI), pp. 1–8.
Again thanks to my co-authors, especially Matteo!
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 first author, Matteo Bortoletto.
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 which deep learning models learn to take shortcuts instead of modeling mental states correctly:
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.
This is a great success and I want to espescially congratulate the first author, Matteo, for his incredible work!
Thanks to all my co-authors!
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 intersection of machine learning, computational cognitive modelling, and human-computer interaction.
We place a special emphasis on modelling core processes of human cognition in computing systems, i.e. we place an emphasis on Machine Theory of Mind.
Techniques used by students in their projects include Multi-Modal Deep Learning, Multi-Agent Reinforcement Learning and biologically plausible learning methods like Spiking Neural Networks.
The course is an advanced master-level one that requires previous experience with deep learning and related fields to enable students to engange with ongoing research in the field of computational human modelling.
