Tobias Huber M.Sc.
Phone: | (+49)(0)821 – 598 2336 |
Email: | |
Room: | 2015 (N) |
Address: | Universitätsstraße 6a, 86159 Augsburg |
Links
Research interest
My research focuses on the explainability of Artificial Intelligence and Reinforcement Learning in particular.
I find it fascinating how Reinforcement Learning algorithms can independently develop strategies based only on observations and rewards. In some cases, the agents even develop new strategies that even humans have not yet considered (
e.g. by the chess computer AlphaZero). However, since only the goal of the agents is defined, it is often not clear what exactly the learned strategies look like. This is exacerbated by the use of modern machine learning techniques, which achieve considerable success but are also very opaque.
The goal of my research is to develop new algorithms that make the behaviour of intelligent agents explainable to users and thus facilitate the cooperation between humans and computers.
Talks
2021
Explainable deep Reinforcement Learning. Invited talk in the CSL Machine Learning Reading Club of the Computational Science Lab (CSL) at the University of Hohenheim, 02.02.2021, Slides .
Teaching
Courses
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WS20/21: Partical Course: Game Development
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WS 19/20: Partical Course: Game Development
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WS 18/19: Partical Course: Game Development
Insights into final projects

Supervised theses
- Exploring an Explainable Reinforcement Learning Design for a Self Learning American Football Simulation (Master, David Makowski, 2020)
- Implementation and Comparison of Occlusion-based Explainable Artificial Intelligence Methods (Bachelor, Benedikt Limmer, 2020, Link)
- Implementierung und Vergleich verschiedener Salienz-Karten Algorithmen für tiefes bestärkendes Lernen (Master, Patrick Bergmoser, 2019)
Publications
2020 |
Katharina Weitz, Dominik Schiller, Ruben Schlagowski, Tobias Huber and Elisabeth André. in press. "Let me explain!": exploring the potential of virtual agents in explainable AI interaction design. Journal on Multimodal User Interfaces DOI: 10.1007/s12193-020-00332-0 |
Simon Flutura, Andreas Seiderer, Tobias Huber, Katharina Weitz, Ilhan Aslan, Ruben Schlagowski, Elisabeth André and Joachim Rathmann. 2020. Interactive machine learning and explainability in mobile classification of forest-aesthetics. In Catia Prandi and Johann Marquez-Barja (Ed.). GoodTechs '20: Proceedings of the 6th EAI International Conference on Smart Objects and Technologies for Social Good, September 2020, Antwerp, Belgium. ACM, New York, NY, 90-95. DOI: 10.1145/3411170.3411225 |
Tobias Huber, Katharina Weitz, Elisabeth André and Ofra Amir. 2020. Local and global explanations of agent behavior: integrating strategy summaries with saliency maps. arXiv:2005.08874, |
Dominik Schiller, Tobias Huber, Michael Dietz and Elisabeth André. 2020. Relevance-based data masking: a model-agnostic transfer learning approach for facial expression recognition. Frontiers in Computer Science 2, 6. DOI: 10.3389/fcomp.2020.00006 |
Silvan Mertes, Tobias Huber, Katharina Weitz, Alexander Heimerl and Elisabeth André. 2020. This is not the texture you are looking for! Introducing novel counterfactual explanations for non-experts using generative adversarial learning. preprint, |
Klaus Weber, Lukas Tinnes, Tobias Huber, Alexander Heimerl, Eva Pohlen, Marc-Leon Reinecker and Elisabeth André. 2020. Towards demystifying subliminal persuasiveness: using XAI-techniques to highlight persuasive markers of public speeches. Lecture Notes in Computer Science 12175, 113-128. DOI: 10.1007/978-3-030-51924-7_7 |
2019 |
Katharina Weitz, Dominik Schiller, Ruben Schlagowski, Tobias Huber and Elisabeth André. 2019. "Do you trust me?" Increasing user-trust by integrating virtual agents in explainable AI interaction design. In Catherine Pelachaud, Jean-Claude Martin, Hendrik Buschmeier, Gale Lucas and Stefan Kopp (Ed.). Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents - IVA '19, Paris, France, July 02 - 05, 2019. ACM Press, New York, NY, 7-9. DOI: 10.1145/3308532.3329441 |
Tobias Huber, Dominik Schiller and Elisabeth André. 2019. Enhancing explainability of deep reinforcement learning through selective layer-wise relevance propagation. Lecture Notes in Computer Science 11793, 188-202. DOI: 10.1007/978-3-030-30179-8_16 |
Stanislava Rangelova, Simon Flutura, Tobias Huber, Daniel Motus and Elisabeth André. 2019. Exploration of physiological signals using different locomotion techniques in a VR adventure game. Lecture Notes in Computer Science 11572, 601-616. DOI: 10.1007/978-3-030-23560-4_44 |
Dominik Schiller, Tobias Huber, Florian Lingenfelser, Michael Dietz, Andreas Seiderer and Elisabeth André. 2019. Relevance-based feature masking: improving neural network based whale classification through explainable artificial intelligence. In Gernot Kubin and Zdravko Kačič (Ed.). Interspeech 2019, 15-19 September 2019, Graz. ISCA, 2423-2427 DOI: 10.21437/interspeech.2019-2707 |
2018 |
Tobias Huber. 2018. Tiefes bestärkendes Lernen: Grundlagen, Approximationseigenschaft und Implementierung multimodaler Erklärungen. Masterarbeit, Universität Augsburg, Universität Augsburg, Augsburg. |