Probability-Enhanced Predictions in the Anticipatory Classifier System

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.

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