Illinois Genetic Algorithms Laboratory

Butz, M.V., Goldberg, D.E., Stolzmann, W. (2000)
TR No.: 2000016 | Download PDF | Download PS

Abstract:
The Anticipatory Classifier System (ACS) recently showed many capabilities
new to the Learning Classifier System field. Due to its enhanced rule
structure with an effect part, it forms an internal environmental
representation, learns latently besides the common reward learning, and
can use many cognitive processes. This paper introduces a
probability-enhancement in the predictions of the ACS which
enables the system to handle different kinds of non-determinism in an
environment. Experiments in two different mazes will show that the ACS
is now able to handle action-noise and irrelevant random attributes in the
perceptions. Furthermore, applications with a recently introduced GA will
reveal the general independence of the two new mechanism as well as the
ability of the GA to substantially decrease the population size.