The key part of my research work is covered by approximate
reasoning with fuzzy systems. The investigations are based on the fact
that humans are able to make reasonable decisions though their domain
knowledge is often only vague or uncertain. The aim of my work is to
find a flexible representation of vague and/or uncertain knowledge and
an efficient way to reason on the basis of the knowledge. I
especially focus on rule based systems.
The approximate reasoning techniques are applied in the
DFG-project Fynesse (FuzzY-NEuro-SyStEm) that deals with autonomous
learning of strategies for optimal sequential decisions in large
domains. The important questions for the fuzzy part are
the integration of
imperfect prior knowledge about the strategy into a reinforcement
learning approach,
the interpretation of a learned strategy
in terms of fuzzy rules (rule extraction).
