Department of Computer Science Technische Universitaet Muenchen
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Martin Sachenbacher
Dr.rer.nat, Dipl.-Inf.
Head of Emmy Noether Research Group
Technische Universität München
E-Mail: sachenba (at) in.tum.de
Telephone: +49 (89) 289-19556

Constraint-based Models and Algorithms for Self-Diagnosis and -Planning

Emmy Noether Research Group funded by DFG

How to represent the behavior of complex technical (or natural) systems in a computer model? How to get from quantitative data (e.g. obtained from sensor measurements) to a high-level, qualitative understanding of the system (e.g. its relevant states and possible transitions)? How to exploit such models to track the state of the system and drive it towards desired states? How to make the models concise enough and the algorithms that operate on them fast enough to equip everyday devices (e.g. cars) with model-based self-diagnosis, self-repair, and self-optimization capabilities?
We develop algorithmic and model-theoretic foundations for "self-aware" systems that can autonomously diagnose, test, repair, and optimize themselves by reasoning from models of their normal and faulty behavior. The models are expressed as constraints that describe feasible states of system components, and (probabilistic or cost-based) transitions between these states. The main challenge is to develop algorithms and representations that allow for efficiently reasoning through a large space of component interactions to quickly identify most likely current states (monitoring and diagnosis) and generate least-cost actions to drive a system towards desired states (planning and reconfiguration). While this is very hard (NP-complete) in general, real-world instances are likely to have structural features in their underlying constraint model that make these tasks tractable and even feasible for on-line, embedded applications. Our approach is therefore to adapt and combine techniques from constraint programming, combinatorial optimization, graph theory, and probabilistic reasoning to exploit model structure and obtain practically effective and scalable reasoning strategies. We seek to apply our methods in the context of programming intelligent embedded systems (such as fault-robust automotive subsystems) and software systems (such as self-configuring web services).
Martin Sachenbacher received both his M.Sc. (1996) and Ph.D. (2001) degrees from Technische Universität München. He has been a doctoral fellow of the Robert Bosch GmbH, Stuttgart (1996-1999) and worked as a postdoc at the Massachusetts Institute of Technology, USA (2002-2005) and Universität München (2005-2006). In 2006, he was awarded an Emmy Noether Grant by Deutsche Forschungsgemeinschaft (DFG) to set up an Independent Junior Research Group at Technische Universität München and its CoTeSys (Cognition in Technical Systems) Cluster of Excellence.