Processing of vague and uncertain knowledge.
Really complex systems can generally not be exactly modeled in
every detail. The other way round, a single datum loses its significance
with rising complexity. Nowadays, many applications are known that
became possible only by neglecting needless details and an adequate
consideration of uncertain data.
Reasonable and precise decisions can be based on vague and uncertain
knowledge. The best example for systems that successfully deal with such
information are human beings. However, in areas like data processing
and technical automation more and more people realize that it is not
only too costly to consider all relevant information but often
impossible. Generally, there are two different areas for the
application of processing fuzzy information:
- applications that are too complex to be realized with classic approaches
- increase of the efficiency in existing applications
We especially research reasoning methods that
- intuitively model fuzzy knowledge,
allow different kinds of information sources
(vague statements, uncertain data, positive/negative information ...),
consequently operate on a high level of abstraction and, therefore,
are very efficient.
These techniques are applied in Fynesse (FuzzY-NEuro-SyStEm), for
example, a hybrid architecture for sequential decision problems like
control of dynamic systems or scheduling problems (job shop
scheduling, instruction scheduling in compiler construction). In
Fynesse, fuzzy knowledge is used to restrict the search space of
potential solutions on the one hand, and to describe found solutions
in a comprehensible form on the other hand. In this way, it becomes
possible to deal with really complex systems.
Projects in this area