Agent Learning Instead of Behavior Implementation for Simulations
� A Case Study Using Classifier Systems
MATES 2008, LNAI 5244, Springer-Verlag, Berlin Heidelberg, (2008)Abstract
Although multi-agent simulations are an intuitive way of
conceptualizing systems that consist of autonomous actors, a major
problem
is the actual design of the agent behavior. In this contribution,
we
examine the potential of using agent-based learning for implementing
the agent behavior. We enhanced SeSAm, a platform for agent-based
simulation, by replacing the usual rule-based agent architecture by
XCS,
a well-known learning classifier system (LCS). The resulting model
is
tested using a simple evacuation scenario. The results show that on
the
one hand side plausible agent behavior could be learned. On the other
hand side, though, the results are quite brittle concerning the frame
of
environmental feedback, perception and action modeling.
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