aMAZEing Programming - Providing SKILLs to Fellow Students. - Timo P. Gros; Pascal Leo Held; Pascal Lauer; Niklas O. Metzger; Kallistos Weis (SKILL, 2019)

Deep Statistical Model Checking. - Timo P. Gros; Holger Hermanns; Jörg Hoffmann 001; Michaela Klauck; Marcel Steinmetz (FORTE, 2020)

TraceVis: Towards Visualization for Deep Statistical Model Checking. - Timo P. Gros; David Groß; Stefan Gumhold; Jörg Hoffmann 001; Michaela Klauck; Marcel Steinmetz (ISoLA (4), 2020)

Tracking the Race Between Deep Reinforcement Learning and Imitation Learning. - Timo P. Gros; Daniel Höller; Jörg Hoffmann 001; Verena Wolf (QEST, 2020)

Lab Conditions for Research on Explainable Automated Decisions. - Christel Baier; Maria Christakis; Timo P. Gros; David Groß; Stefan Gumhold; Holger Hermanns; Jörg Hoffmann 001; Michaela Klauck (TAILOR, 2020)

Real-Time Decision Making for a Car Manufacturing Process Using Deep Reinforcement Learning. - Timo P. Gros; Joschka Groß; Verena Wolf (WSC, 2020)

Tracking the Race Between Deep Reinforcement Learning and Imitation Learning - Extended Version. - Timo P. Gros; Daniel Höller; Jörg Hoffmann 001; Verena Wolf (CoRR, 2020)

DSMC Evaluation Stages: Fostering Robust and Safe Behavior in Deep Reinforcement Learning. - Timo P. Gros; Daniel Höller; Jörg Hoffmann 001; Michaela Klauck; Hendrik Meerkamp; Verena Wolf (QEST, 2021)

Artifact of "MoGym: Using Formal Models for Training and Verifying Decision Agents". - Timo P. Gros; Holger Hermanns; Michaela Klauck; Maximilian A. Köhl (Zenodo, 2021)

Debugging a Policy: Automatic Action-Policy Testing in AI Planning. - Marcel Steinmetz; Daniel Fiser; Hasan Ferit Eniser; Patrick Ferber; Timo P. Gros; Philippe Heim; Daniel Höller; Xandra Schuler; Valentin Wüstholz; Maria Christakis; Jörg Hoffmann 001 (ICAPS, 2022)

MoGym: Using Formal Models for Training and Verifying Decision-making Agents. - Timo P. Gros; Holger Hermanns; Jörg Hoffmann 001; Michaela Klauck; Maximilian A. Köhl; Verena Wolf (CAV (2), 2022)

Metamorphic relations via relaxations: an approach to obtain oracles for action-policy testing. - Hasan Ferit Eniser; Timo P. Gros; Valentin Wüstholz; Jörg Hoffmann 001; Maria Christakis (ISSTA, 2022)

Glyph-Based Visual Analysis of Q-Leaning Based Action Policy Ensembles on Racetrack. - David Groß; Michaela Klauck; Timo P. Gros; Marcel Steinmetz; Jörg Hoffmann 001; Stefan Gumhold (IV, 2022)

Artifact for the Tool Paper: MoGym: Using Formal Models for Training and Verifying Decision-making Agents. - Timo P. Gros; Holger Hermanns; Jörg Hoffmann 001; Michaela Klauck; Maximilian A. Köhl; Verena Wolf (Zenodo, 2022)

XAI Requirements in Smart Production Processes: A Case Study. - Deborah Baum; Kevin Baum 001; Timo P. Gros; Verena Wolf (xAI (1), 2023)

Analyzing neural network behavior through deep statistical model checking. - Timo P. Gros; Holger Hermanns; Jörg Hoffmann 001; Michaela Klauck; Marcel Steinmetz (Int. J. Softw. Tools Technol. Transf., 2023)

DSMC Evaluation Stages: Fostering Robust and Safe Behavior in Deep Reinforcement Learning - Extended Version. - Timo P. Gros; Joschka Groß; Daniel Höller; Jörg Hoffmann 001; Michaela Klauck; Hendrik Meerkamp; Nicola J. Müller; Lukas Schaller; Verena Wolf (ACM Trans. Model. Comput. Simul., 2023)

Safe Reinforcement Learning Through Regret and State Restorations in Evaluation Stages. - Timo P. Gros; Nicola J. Müller; Daniel Höller; Verena Wolf (Principles of Verification (3), 2024)

Acting for the Right Reasons: Creating Reason-Sensitive Artificial Moral Agents. - Kevin Baum 001; Lisa Dargasz; Felix Jahn; Timo P. Gros; Verena Wolf (CoRR, 2024)

Motion Primitives as the Action Space of Deep Q-Learning for Planning in Autonomous Driving. - Tristan Schneider; Matheus V. A. Pedrosa; Timo P. Gros; Verena Wolf; Kathrin Flaßkamp (IEEE Trans. Intell. Transp. Syst., 2024)

Per-Domain Generalizing Policies: On Validation Instances and Scaling Behavior. - Timo P. Gros; Nicola J. Müller; Daniel Fiser; Isabel Valera; Verena Wolf; Jörg Hoffmann 001 (CoRR, 2025)