Timo P. Gros
Group Leader NEXD
PhD Student
DFKI – Neuro-Mechanistic Modeling
Modeling and Simulation Group, Saarland University
Foundations of Artificial Intelligence, Saarland University
Saarland Informatics Campus
Campus D3.2 (DFKI), Room 2.14
66123 Saarbrücken
Germany
About Me
I am a Ph.D. student in the Modeling and Simulation group of Prof. Dr. Verena Wolf and in the Foundations of Artificial Intelligence group of Prof. Dr. Jörg Hoffmann. I am also a member of the Graduate School of Computer Science. Since December 2023, I work at the German Research Center for Artificial Intelligence (DFKI). In particular, I am involved in the Centre for European Research in Trusted AI (CERTAIN) and the department of Neuro-mechanistic Modeling (NMM).
Other Work
I have reviewed manuscripts for ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (PADS) 2020, Quantitative Evaluation of SysTems (QEST) 2020 – 2024, the 7th IFAC Conference on Analysis and Design of Hybrid Systems (ADHS) 2021, the ACM International Conference on Hybrid Systems: Computation and Control (HSCC) 2022 – 2024, the Artificial Intelligence Journal (AIJ), and the International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2024.
I was a co-organizer of the Bridging the Gap Between AI Planning and Reinforcement Learning (PRL) workshop at ICAPS 2023 and IJCAI 2023. I am a co-organizer of the PRL workshop at ICAPS 2024 and of the Workshop on Reliable Data-Driven Planning and Scheduling at ICAPS 2024.
Interests
- Safe Reinforcement Learning
- Deep Reinforcement Learning
- AI Planning
- Statistical Model Checking
Education
PhD in Computer Science (Artificial Intelligence), ongoing
Saarland University
Master's Thesis, 2021
Saarland University
Tracking the Race: Analyzing Racetrack Agents Trained with Imitation Learning and Deep Reinforcement Learning
Bachelor's Thesis, 2018
Saarland University
Markov Automata taken by Storm
Publications (26)
2026
Per-Domain Generalizing Policies: On Learning Efficient and Robust Q-Value Functions
2026 — Proceedings of the Thirty-Sixth International Conference on Automated Planning and Scheduling. International Conference on Automated Planning and Scheduling (ICAPS-2026), June 27 - July 2, Dublin, Ireland
PGTG: Procedurally Generated Grid-Based Traffic Gym
2026 — Reinforcement Learning Journal
2026 — Workshop on Planning in the Era of LLMs at ICAPS-26
2025
Learning Per-Domain Generalizing Policies Using Offline Reinforcement Learning
2025 — Proceedings of the Ninth Workshop on Bridging the Gap Between AI Planning and (Reinforcement) Learning (PRL @ ICAPS 2025). Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL-2025), located at ICAPS'25, November 10, Melbourne, Australia
PyDSMC: Statistical Model Checking for Neural Agents Using the Gymnasium Interface
2025 — Quantitative Evaluation of Systems and Formal Modeling and Analysis of Timed Systems - Second International Joint Conference, Proceedings. International Joint Conference on Quantitative Evaluation of Systems and Formal Modeling and Analysis of Timed Systems (QEST+FORMATS-2025), August 26-28, Aarhus, Denmark
Visual Analysis of Action Policy Behavior: A Case Study in Grid-World Driving
2025 — World Conference on Explainable Artificial Intelligence. xAI: World Conference on Explainable Artificial Intelligence (xAI-2025), July 9-11, Istanbul, Turkey
Bridging the Gap Between AI Planning and Reinforcement Learning
2025 — AAAI 2025 Workshop Proceedings (PRL Workshop). Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL-2025), February 25
Per-Domain Generalizing Policies: On Validation Instances and Scaling Behavior
2025 — The 35th International Conference on Automated Planning and Scheduling. International Conference on Automated Planning and Scheduling (ICAPS-2025), November 9-14, Melbourne, Australia
2024
Acting for the Right Reasons: Creating Reason-Sensitive Artificial Moral Agents
2024 — ArXiv e-prints (arxiv)
Motion Primitives as the Action Space of Deep Q-Learning for Planning in Autonomous Driving
2024 — IEEE Transactions on Intelligent Transportation Systems
Comparing State-of-the-art Graph Neural Networks and Transformers for General Policy Learning
2024 — ICAPS Workshop on Planning and Reinforcement Learning (PRL)
Safe Reinforcement Learning Through Regret and State Restorations in Evaluation Stages
2024 — Workshop on Reliable Data-Driven Planning and Scheduling. International Conference on Automated Planning and Scheduling (ICAPS-2024)
2023
2023 — ACM Transactions on Modeling and Computer Simulation (TOMACS)
Analyzing neural network behavior through deep statistical model checking
2023 — International Journal on Software Tools for Technology Transfer (STTT)
XAI Requirements in Smart Production Processes: A Case Study
2023 — Explainable Artificial Intelligence, First World Conference, Proceedings, Part I. xAI: World Conference on Explainable Artificial Intelligence (xAI-2023), July 26-28, Lissabon, Portugal
2022
Metamorphic relations via relaxations: An approach to obtain oracles for action-policy testing
2022 — ISSTA 2022: Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis. ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA-2022), July 18-22
Debugging a Policy: Automatic Action-Policy Testing in AI Planning
2022 — Proceedings of the 32nd International Conference on Automated Planning and Scheduling. International Conference on Automated Planning and Scheduling (ICAPS-2022), July 13-24
Glyph-Based Visual Analysis of Q-Learning Based Action Policy Ensembles on Racetrack
2022 — International Conference on Information Visualisation (IV'22). International Conference on Information Visualisation (IV-2022), 26th International Conference on Information Visualisation, July 19-22, Vienna, Austria
MoGym: Using Formal Models for Training and Verifying Decision-making Agents
2022 — International Conference on Computer Aided Verification. International Conference on Computer Aided Verification (CAV)
2021
Debugging a Policy: A Framework for Automatic Action Policy Testing
2021 — Bridging the Gap Between AI Planning and Reinforcement Learning (PRL) – Workshop at ICAPS 2021 (August 5-6). Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL-2021), located at ICAPS 2021, August 5-6
Lab Conditions for Research on Explainable Automated Decisions
2021 — Trustworthy AI–Integrating Learning, Optimization and Reasoning: First International Workshop, TAILOR 2020, Virtual Event, September 4–5, 2020, Revised Selected Papers. Workshop on Foundations of Trustworthy AI integrating Learning, Optimisation and Reasoning (TAILOR-2020), located at First International Workshop, April 4-5, Virtual Event
TraceVis: towards visualization for deep statistical model checking
2021 — Leveraging Applications of Formal Methods, Verification and Validation: Tools and Trends: 9th International Symposium on Leveraging Applications of Formal Methods, ISoLA 2020. International Symposium On Leveraging Applications of Formal Methods, Verification and Validation (ISoLA-2020), 9th International Symposium on Leveraging Applications of Formal Method, October 20-30, Rhodes, Greece
2020
Tracking the Race Between Deep Reinforcement Learning and Imitation Learning - Extended Version
2020 — ArXiv e-prints (arxiv)
Deep statistical model checking
2020 — Formal Techniques for Distributed Objects, Components, and Systems: 40th IFIP WG 6.1 International Conference, FORTE 2020, Held as Part of the 15th International Federated Conference on Distributed Computing Techniques, DisCoTec 2020, Valletta, Malta. Distributed Applications and Interoperable Systems: IFIP WG 6.1 International Conference (DAIS-2020), located at 5th International Federated Conference on Distributed Computing Techniques, June 15-19, Valetta, Malta
Real-Time Decision Making for a Car Manufacturing Process Using Deep Reinforcement Learning
2020 — 2020 Winter Simulation Conference (WSC). Winter Simulation Conference (WSC-2020), December 14-18, Orlando,, FL, USA
2019
aMAZEing Programming - Providing SKILLs to Fellow Students
2019 — SKILL 2019 - Studierendenkonferenz Informatik. Bonn: Gesellschaft für Informatik e.V., 2019. Studierendenkonferenz Informatik (SKILL-2019), September 25-26, Kassel, Germany
Teaching (14)
Special Topics in Deep Reinforcement Learning
Assistant, in Summer 2024
Saarland UniversityDeep Reinforcement Learning
Assistant, in Winter 2022/23
Saarland UniversityDeep Reinforcement Learning
Assistant, in Summer 2021
Saarland UniversityConcurrent Programming
Assistant, in Summer 2021
Saarland UniversityStatistics Lab
Assistant, in Summer 2020
Saarland UniversityReinforcement Learning
Assistant, in Winter 2019/20
Saarland UniversityProbabilistic Models and Data Analysis
Assistant, in Summer 2019
Saarland UniversityConcurrent Programming
Assistant, in Summer 2018
Saarland UniversityProgramming 2
Supervision Tutor, in Summer 2017
Saarland UniversityProgramming 2
Supervision Tutor, in Summer 2016
Saarland UniversityBasics of Algorithms and Data Structures
Tutor, in Winter 2015/16
Saarland UniversityProgramming 2
Tutor, in Summer 2015
Saarland UniversityMathematics for Engineers 1
Tutor, in Winter 2014/15
Saarland UniversityProgramming 1
Tutor, in Winter 2013/14
Saarland University
Supervised Students (20)
Ivo Hoese
Bachelor's Thesis, ongoing
Dennis Heß
Bachelor's Thesis, ongoing
Philipp Sauer
Classification and Analysis of Safe Reinforcement Learning Algorithms
Master's Thesis, 2025
Michel Scherer
Lifting the RARE Algorithm to Continuous State and Action Spaces
Bachelor's Thesis, 2025
Jan Kautenburger
GNPGNorks – Generalized Neural Policies Using Graph Neural Networks
Bachelor's Thesis, 2024
Jan-Paul Schulz
Extending the Racetrack Environment
Master's Thesis, 2024
Alexander Kunz
Using Deep Reinforcement Learning in the Steel Industry: A Game of Iron and Steel
Bachelor's Thesis, 2023
Michael Kormann
Reinforcement Learning with Reward Shaping for Planning Domains in Jani
Bachelor's Thesis, 2022
Ilka Kopping
Performance Comparison of Deep Reinforcement Learning Algorithms for the ProGen Benchmark
Master's Thesis, 2022
Tristan Schneider
Motion Primitives for Motion Planning with Reinforcement Learning
Bachelor's Thesis, 2022
Luis Paulus
Mapping Instructions to Ports: A Reinforcement Curriculum Learning Approach
Bachelor's Thesis, 2021
Jeremias Wagner
Deep Reinforcement Learning to Battle Snake
Bachelor's Thesis, 2020