Chair for Human-Centered Artificial Intelligence

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  8. Tobias Huber

Tobias Huber M.Sc.

Research assistant
Chair for Human-Centered Artificial Intelligence
Phone: (+49)(0)821 – 598 2336
Email: tobias.huber@informatik.uni-augsburg.de
Room: 2015 (N)
Address: Universitätsstraße 6a, 86159 Augsburg

Links

Publications
Networking
Ohter

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 .

Press

Teaching

Courses

Insights into final projects

You can watch and even play student projects from the game desigen lectures here.

 

© University of Augsburg

Supervised theses

  • Design und Implementierung einer Virtual-Reality-Umgebung zum Testen des impliziten Lernens von motorischen sequentiellen Bewegungen (Master, 2021, Link)

  • Using Reinforcement Learning to facilitate Implicit Learning in a VR Sports Simulation (Bachelor, 2021)

  • Understanding Subliminal Persuasive Body Language in Political Speeches via ExplainableArtificial Intelligence (Bachelor, 2021)

  • Verbinden von Belohnungs-Zerlegung und Strategie-Zusammenfassung für erklärbares Bestärkendes Lernen (Bachelor, 2021)
  • Exploring an Explainable Reinforcement Learning Design for a Self Learning American Football Simulation (Master, 2020)
  • Implementation and Comparison of Occlusion-based Explainable Artificial Intelligence Methods (Bachelor, 2020, Link)
  • Implementierung und Vergleich verschiedener Salienz-Karten Algorithmen für tiefes bestärkendes Lernen (Master, 2019)

Peer-Review

I was reviewer for the following conferences:

PC Member / Editorials

Publications

Huber, Tobias
2022 | 2021 | 2020 | 2019 | 2018

2022

Silvan Mertes, Christina Karle, Tobias Huber, Katharina Weitz, Ruben Schlagowski and Elisabeth André. in press. Alterfactual explanations: the relevance of irrelevance for explaining AI systems. preprint. DOI: 10.48550/arXiv.2207.09374
PDF | BibTeX | RIS | DOI

Tobias Huber, Benedikt Limmer and Elisabeth André. 2022. Benchmarking perturbation-based saliency maps for explaining Atari agents. Frontiers in Artificial Intelligence 5, 903875. DOI: 10.3389/frai.2022.903875
PDF | BibTeX | RIS | DOI

Pooja Prajod, Dominik Schiller, Tobias Huber and Elisabeth André. 2022. Do deep neural networks forget facial action units? - Exploring the effects of transfer learning in health related facial expression recognition. In Arash Shaban-Nejad, Martin Michalowski and Simone Bianco (Ed.). AI for disease surveillance and pandemic intelligence: intelligent disease detection in action. Springer, Cham (Studies in Computational Intelligence ; 1013), 217-233. DOI: 10.1007/978-3-030-93080-6_16
PDF | BibTeX | RIS | DOI

Silvan Mertes, Tobias Huber, Katharina Weitz, Alexander Heimerl and Elisabeth André. 2022. GANterfactual - counterfactual explanations for medical non-experts using generative adversarial learning. Frontiers in Artificial Intelligence 5, 825565. DOI: 10.3389/frai.2022.825565
PDF | BibTeX | RIS | DOI

2021

Tobias Huber, Silvan Mertes, Stanislava Rangelova, Simon Flutura and Elisabeth André. 2021. Dynamic difficulty adjustment in virtual reality exergames through experience-driven procedural content generation. In Keeley Crockett, Sanaz Mostaghim, Dipti Srinivasan and Anna Wilbik (Ed.). 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 5-7 December 2021, Orlando, FL, USA. IEEE, Piscataway, NJ, 1-8. DOI: 10.1109/ssci50451.2021.9660086
PDF | BibTeX | RIS | DOI

Tobias Huber, Katharina Weitz, Elisabeth André and Ofra Amir. 2021. Local and global explanations of agent behavior: integrating strategy summaries with saliency maps. Artificial Intelligence 301, 103571. DOI: 10.1016/j.artint.2021.103571
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2020

Katharina Weitz, Dominik Schiller, Ruben Schlagowski, Tobias Huber and Elisabeth André. 2020. "Let me explain!": exploring the potential of virtual agents in explainable AI interaction design. Journal on Multimodal User Interfaces 15, 87-98. DOI: 10.1007/s12193-020-00332-0
PDF | BibTeX | RIS | DOI

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
PDF | BibTeX | RIS | DOI

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
PDF | BibTeX | RIS | DOI

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
PDF | BibTeX | RIS | DOI

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
PDF | BibTeX | RIS | DOI

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
PDF | BibTeX | RIS | DOI

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
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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
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2018

Tobias Huber. 2018. Tiefes bestärkendes Lernen: Grundlagen, Approximationseigenschaft und Implementierung multimodaler Erklärungen. Masterarbeit, Universität Augsburg. Universität Augsburg, Augsburg.
PDF | BibTeX | RIS

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