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Constraint-based Models and Algorithms for Self-Diagnosis and -Planning |
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| 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?
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| 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).
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| 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.
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