Constantin Ruhdorfer

Institute for Visualisation and Interactive Systems, University of Stuttgart

  Pfaffenwaldring 5a, 70569 Stuttgart, Germany University of Stuttgart, SimTech Building, Room 01.035 Google Scholar

Biography

Constantin Ruhdorfer is a PhD student in the Perceptual User Interfaces group and a PhD scholar in the International Max Planck Research School for Intelligent Systems (IMPRS-IS) since November 2023. He holds a Bachelor’s degree in Computer Science from the Cooperative State University Baden-Württemberg and a Master's degree in Computer Science from the University of Stuttgart. His research interests lies in building and training agents that can cooperate well with humans by taking into account their individual mental states and preferences. Likewise his interests lie at the intersection of multi-agent reinforcement learning, inverse reinforcement learning, machine theory of mind and AI alignment.

Teaching

2023

Fachpraktikum -- Practical Course Interactive Systems: Computational Theory of Mind and Cognition (Tutor)Master

2023

Machine Perception and Learning (Tutor) (winter)Master

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Supervision

2024

Leveraging Biologically-plausible Representations for Robust and Efficient Generalization in Reinforcement Learning (M.Sc.) Nan Jiang*

 

Open-Ended Learning via Auto-Curricula for Solving Zero-Shot Theory of Mind Tasks (M.Sc.) Benjamin Caddell

 

The Yokai Challenge: A New Frontier for Multi-Agent Reinforcement Learning and Machine Theory of Mind (M.Sc.) Rui Yang

2023

Evaluation of Different Image Representations for Reinforcement Learning Agents (M.Sc.) Jayakumar Ramasamy Sundararaj*

*Co-supervised with Anna Penzkofer

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Open Thesis Projects

 

Cognitive Attention Guided Imitation LearningMaster

 

Probing for Theory of Mind in the Multi-Agent Cooperation Game YokaiMaster

 

Cognitive Attention Guided Imitation LearningMaster

 

Spiking Neural Networks for Generalization in RLMaster

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Publications

(Ruhdorfer et al., 2024; Bortoletto et al., 2024; Bortoletto et al., 2024; Bortoletto et al., 2024; Wang et al., 2024)

1. 

   VisRecall++: Analysing and Predicting Visualisation Recallability from Gaze Behaviour

   Yao Wang, Yue Jiang, Zhiming Hu, Constantin Ruhdorfer, Mihai Bâce, Andreas Bulling

   Proc. ACM on Human-Computer Interaction (PACM HCI), 8(ETRA), pp. 1–18, 2024.

   Abstract Links BibTeX Project

   Question answering has recently been proposed as a promising means to assess the recallability of information visualisations. However, prior works are yet to study the link between visually encoding a visualisation in memory and recall performance. To fill this gap, we propose VisRecall++ – a novel 40-participant recallability dataset that contains gaze data on 200 visualisations and five question types, such as identifying the title, and finding extreme values.We measured recallability by asking participants questions after they observed the visualisation for 10 seconds.Our analyses reveal several insights, such as saccade amplitude, number of fixations, and fixation duration significantly differ between high and low recallability groups.Finally, we propose GazeRecallNet – a novel computational method to predict recallability from gaze behaviour that outperforms several baselines on this task.Taken together, our results shed light on assessing recallability from gaze behaviour and inform future work on recallability-based visualisation optimisation.

   doi: 10.1145/3655613

   Paper: wang24_etra.pdf

   Supplementary Material: wang24_etra_sup.pdf

   @article{wang24_etra, title = {VisRecall++: Analysing and Predicting Visualisation Recallability from Gaze Behaviour}, author = {Wang, Yao and Jiang, Yue and Hu, Zhiming and Ruhdorfer, Constantin and Bâce, Mihai and Bulling, Andreas}, year = {2024}, journal = {Proc. ACM on Human-Computer Interaction (PACM HCI)}, pages = {1--18}, volume = {8}, number = {ETRA}, doi = {10.1145/3655613} }

1. Explicit Modelling of Theory of Mind for Belief Prediction in Nonverbal Social Interactions

   Matteo Bortoletto, Constantin Ruhdorfer, Lei Shi, Andreas Bulling

   Proc. 27th European Conference on Artificial Intelligence (ECAI), pp. 1–8, 2024.

   Abstract Links BibTeX Project

   We propose MToMnet - a Theory of Mind (ToM) neural network for predicting beliefs and their dynamics during human social interactions from multimodal input. ToM is key for effective nonverbal human communication and collaboration, yet, existing methods for belief modelling have not included explicit ToM modelling or have typically been limited to one or two modalities. MToMnet encodes contextual cues (scene videos and object locations) and integrates them with person-specific cues (human gaze and body language) in a separate MindNet for each person. Inspired by prior research on social cognition and computational ToM, we propose three different MToMnet variants: two involving fusion of latent representations and one involving re-ranking of classification scores. We evaluate our approach on two challenging real-world datasets, one focusing on belief prediction, while the other examining belief dynamics prediction. Our results demonstrate that MToMnet surpasses existing methods by a large margin while at the same time requiring a significantly smaller number of parameters. Taken together, our method opens up a highly promising direction for future work on artificial intelligent systems that can robustly predict human beliefs from their non-verbal behaviour and, as such, more effectively collaborate with humans.

   doi:

   @inproceedings{bortoletto24_ecai, author = {Bortoletto, Matteo and Ruhdorfer, Constantin and Shi, Lei and Bulling, Andreas}, title = {Explicit Modelling of Theory of Mind for Belief Prediction in Nonverbal Social Interactions}, booktitle = {Proc. 27th European Conference on Artificial Intelligence (ECAI)}, year = {2024}, pages = {1--8}, doi = {} }
2. 

   Benchmarking Mental State Representations in Language Models

   Matteo Bortoletto, Constantin Ruhdorfer, Lei Shi, Andreas Bulling

   Proc. ICML 2024 Workshop on Mechanistic Interpretability, pp. 1–21, 2024.

   Abstract Links BibTeX Project

   While numerous works have assessed the generative performance of language models (LMs) on tasks requiring Theory of Mind reasoning, research into the models’ internal representation of mental states remains limited. Recent work has used probing to demonstrate that LMs can represent beliefs of themselves and others. However, these claims are accompanied by limited evaluation, making it difficult to assess how mental state representations are affected by model design and training choices. We report an extensive benchmark with various LM types with different model sizes, fine-tuning approaches, and prompt designs to study the robustness of mental state representations and memorisation issues within the probes. Our results show that the quality of models’ internal representations of the beliefs of others increases with model size and, more crucially, with fine-tuning. We are the first to study how prompt variations impact probing performance on theory of mind tasks. We demonstrate that models’ representations are sensitive to prompt variations, even when such variations should be beneficial. Finally, we complement previous activation editing experiments on Theory of Mind tasks and show that it is possible to improve models’ reasoning performance by steering their activations without the need to train any probe.

   doi:

   Paper: bortoletto24_icmlw.pdf

   Code: https://git.hcics.simtech.uni-stuttgart.de/public-projects/mental-states-in-LMs

   Paper Access: https://openreview.net/forum?id=yEwEVoH9Be

   @inproceedings{bortoletto24_icmlw, author = {Bortoletto, Matteo and Ruhdorfer, Constantin and Shi, Lei and Bulling, Andreas}, title = {Benchmarking Mental State Representations in Language Models}, booktitle = {Proc. ICML 2024 Workshop on Mechanistic Interpretability}, year = {2024}, pages = {1--21}, doi = {}, url = {https://openreview.net/forum?id=yEwEVoH9Be} }
3. 

   Limits of Theory of Mind Modelling in Dialogue-Based Collaborative Plan Acquisition

   Matteo Bortoletto, Constantin Ruhdorfer, Adnen Abdessaied, Lei Shi, Andreas Bulling

   Proc. 62nd Annual Meeting of the Association for Computational Linguistics (ACL), pp. 1–16, 2024.

   Abstract Links BibTeX Project

   Recent work on dialogue-based collaborative plan acquisition (CPA) has suggested that Theory of Mind (ToM) modelling can improve missing knowledge prediction in settings with asymmetric skill-sets and knowledge. Although ToM was claimed to be important for effective collaboration, its real impact on this novel task remains under-explored. By representing plans as graphs and by exploiting task-specific constraints we show that, as performance on CPA nearly doubles when predicting one’s own missing knowledge, the improvements due to ToM modelling diminish. This phenomenon persists even when evaluating existing baseline methods. To better understand the relevance of ToM for CPA, we report a principled performance comparison of models with and without ToM features. Results across different models and ablations consistently suggest that learned ToM features are indeed more likely to reflect latent patterns in the data with no perceivable link to ToM. This finding calls for a deeper understanding of the role of ToM in CPA and beyond, as well as new methods for modelling and evaluating mental states in computational collaborative agents.

   doi:

   Paper: bortoletto24_acl.pdf

   Code: https://git.hcics.simtech.uni-stuttgart.de/public-projects/limits-of-tom

   @inproceedings{bortoletto24_acl, author = {Bortoletto, Matteo and Ruhdorfer, Constantin and Abdessaied, Adnen and Shi, Lei and Bulling, Andreas}, title = {Limits of Theory of Mind Modelling in Dialogue-Based Collaborative Plan Acquisition}, booktitle = {Proc. 62nd Annual Meeting of the Association for Computational Linguistics (ACL)}, year = {2024}, pages = {1--16}, doi = {} }

1. The Overcooked Generalisation Challenge

   Constantin Ruhdorfer, Matteo Bortoletto, Anna Penzkofer, Andreas Bulling

   arxiv:2406.17949, pp. 1-25, 2024.

   Abstract Links BibTeX Project

   We introduce the Overcooked Generalisation Challenge (OGC) - the first benchmark to study agents’ zero-shot cooperation abilities when faced with novel partners and levels in the Overcooked-AI environment. This perspective starkly contrasts a large body of previous work that has trained and evaluated cooperating agents only on the same level, failing to capture generalisation abilities required for real-world human-AI cooperation. Our challenge interfaces with state-of-the-art dual curriculum design (DCD) methods to generate auto-curricula for training general agents in Overcooked. It is the first cooperative multi-agent environment specially designed for DCD methods and, consequently, the first benchmarked with state-of-the-art methods. It is fully GPU-accelerated, built on the DCD benchmark suite minimax, and freely available under an open-source license: this https URL. We show that current DCD algorithms struggle to produce useful policies in this novel challenge, even if combined with recent network architectures that were designed for scalability and generalisability. The OGC pushes the boundaries of real-world human-AI cooperation by enabling the research community to study the impact of generalisation on cooperating agents.

   Code: https://git.hcics.simtech.uni-stuttgart.de/public-projects/OGC

   Paper Access: https://arxiv.org/abs/2406.17949

   @techreport{ruhdorfer2024_arxiv, title = {The Overcooked Generalisation Challenge}, author = {Ruhdorfer, Constantin and Bortoletto, Matteo and Penzkofer, Anna and Bulling, Andreas}, year = {2024}, pages = {1-25}, url = {https://arxiv.org/abs/2406.17949} }