SelbstĂ¤ndiges Lernen von Regelstrategien.
FYNESSE (FuzzY-NEuro-SyStEm) is an architecture that autonomously
learns to make optimal sequential decisions in large domains. This
problem of decision making is often encountered, e.g. control of
dynamical systems or job shop scheduling. In contrary to supervised
learning where an expert is needed to obtain desired input-output
patterns for the decision system our reinforcement learning approach
derives the control strategy in direct interaction with the process
from success and failure.
Publikationen zum Projekt