Illinois Genetic Algorithms Laboratory

Butz, M., Goldberg, D.E., Stolzmann, W. (1999)
TR No.: 99019 | Download PDF | Download PS

Abstract:

An Anticipatory Classifier System (ACS) is a learning mechanism based on learning classifier systems and the cognitive model of “Anticipatory Behavioral Control”.
By comparing perceived consequences with its own expectations (anticipations), an ACS is able to learn in multi-step environments. To date, the ACS has proven its abilities in various problems of that kind. It is able to learn latently (i.e. to learn without getting any reward) and it is able to distinguish between non-Markov states.
Additionally, an ACS is capable of incrementally building a cognitive map that can be used to do action-planning. Although the ACS has proven to scale up in suitable environments, it depends on certain environmental properties. It believes itself to be the only agent that can change the perceptions received from an environment. Any environmental change is considered and believed to be caused by the executed actions. The ACS learns from the changes by using fixed mechanisms. This paper reveals the properties of an environment that the current ACS assumes to be given. By investigating the problems of the current ACS when violating these properties we believe that this investigation will immediately serve for a better understanding of the ACS and lead to many ideas to improve the current ACS. We will propose some ideas and discuss the important ones in more detail.