Dagstuhl Seminar 23492
Model Learning for Improved Trustworthiness in Autonomous Systems
( Dec 03 – Dec 08, 2023 )
- Ellen Enkel (Universität Duisburg-Essen, DE)
- Nils Jansen (Radboud University Nijmegen, NL)
- Mohammad Reza Mousavi (King's College London, GB)
- Kristin Yvonne Rozier (Iowa State University - Ames, US)
- Marsha Kleinbauer (for scientific matters)
- Simone Schilke (for administrative matters)
Autonomous systems increasingly enter our everyday life. Consequently, there is a strong need for safety, correctness, trust, and explainability. Well-defined models with clear semantics pose a convenient way to address these requirements. The area of model learning provides a structured way to obtain models from data. However, autonomous systems operate in the real world and pose challenges that go beyond the state-of-the-art in model learning.
The technical challenges addressed in this Dagstuhl Seminar are system evolution and adaptations, learning heterogeneous models (addressing aspects such as discrete and continuous behaviors, stochastic, and epistemic uncertainty), and compositional learning. Our vision is that model learning is a key enabler solving the bottleneck of lack of specifications and models in various typical applications and hence, our seminar will address fundamental challenges to enable impact in a number of application areas.
We will bring together experts in (1) the domain of robotic and autonomous systems, (2) the technical methods of model learning, and (3) the applications of model learning. These include domain experts in robotics (planning, physical design and validation) and autonomous systems. Technical methods include automata learning, synthesis of logical specifications, statistical model learning, machine learning, system identification, and process mining. Application experts include validation and verification, transparency and trust, and explainability.
With this Dagstuhl Seminar, we want to actively encourage the interaction between experts and young researchers in the interdisciplinary areas of artificial intelligence, software engineering, autonomous systems, and human factors both from academia and industry. Contentwise, we emphasize the following directions:
- model learning techniques for AI-enabled autonomous systems: This involves recent techniques for learning models of evolving and variability-intensive systems;
- application of model-learning to increase transparency and trust in robotics and autonomous system.
We identify the following technical and multi-disciplinary research questions:
- How can we efficiently learn about system evolution and adaptation?
- How can we learn heterogeneous models, possibly by separating orthogonal concerns?
- How can we scale the mode learning?
- How can adaptive model learning be used to focus the validation and verification effort in evolving systems?
- How can learn model contribute to trust in autonomous systems?
- What types of models can be used to provide understandable explanations for AI-enabled and autonomous systems?
- Computers and Society
- Logic in Computer Science
- Machine Learning
- Autonomous Systems
- Machine Learning
- Artificial Intelligence
- Formal Methods
- Automata Learning
- Software Evolution
- Technology Acceptance
- Self-Adaptive Systems
- Cyber-physical Systems
- Safety-critical Systems