Introducing a Genetic Generalization Pressure to the Anticipatory Classiier System Part 1: Theoretical Approach Introducing a Genetic Generalization Pressure to the Anticipatory Classiier System Part 1: Theoretical Approach

The Anticipatory Classiier System is a learning classiier system that is based on the cog-nitive mechanism of anticipatory behavioral control. Besides the common reward learning, the ACS is able to learn latently (i.e. to learn in an environment without getting any reward) which is not possible with reinforcement learning techniques. Furthermore, it is forming a complete internal representation of the environment and thus, it is able to use cognitive processes such as reasoning and planning. Latest research showed that there are problems that challenge the current ACS learning mechanism. It was observed that the ACS is not generating accurate, maximally general rules reliably (i.e. rules which are accurate and in the mean time as general as possible), but it is sometimes generating over-speciic rules. This paper shows how a genetic algorithm can be used to overcome this present pressure of over-speciication in the ACS mechanism with a genetic generalization pressure. The ACS works then as a hybrid which learns latently, forms a cognitive map, and evolves accurate, maximally general rules.