40 Jahre Informatik in München - Fakultät für Informatik - TUM

Forschungsgebiete der Professur
Model-based systems aim at automatically generating, rather than
programming, problem solvers. This is achieved by combining a domainindependent
algorithm and a model of the subject (e.g. a physical device).
The foundation for this work is provided by Artificial Intelligence research on
formal theories of generic problem solvers and conceptual modeling. The
research comprises:
Systems for automated fault analysis, diagnosis, testing, and repair of
technical systems
This includes the automated generation of diagnostic hypotheses, proposed
tests and repair actions.
Models are also used to
support activities during
design, such as diagnosability
analysis, sensor
placement, and Failure
Mode Effect Analysis
(FMEA). Prototypes of
such systems have been
developed for applications
in the automotive and
aeronautics industries.
Model-based behavior prediction, situation assessment and therapy
proposal for natural and technical processes, in particular ecological
systems
Assessing the situation of an ecosystem from a limited number of
observations is a challenging diagnosis task. Additionally, one wants to reason
about possible cures or symptom treatments in order to influence the
ecosystem in a direction that is in accordance with some specified goals. We
have developed theoretical foundations and systems for modeling such
systems and for an extended model-based diagnosis task and apply the results,
for instance, to water treatment problems.
Automated and qualitative modeling
Many tasks, for example in diagnosis or during early stages of design, have to
be performed without precise numerical information, and sometimes,
knowledge about functional interdependencies underlying a class of systems
(e.g. ecosystems) is partial and qualitative. We develop and apply methods
and systems for representing and reasoning with incomplete knowledge about
physical systems. This includes the automated composition of models from
model fragments (“compositional modeling”) and the automated generation of
qualitative models from numerical models.
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