Wolfgang Stolzmann: An Introduction to Anticipatory Classifier Systems
Anticipatory Classifier Systems (ACS) are classifier systems that learn by using the cognitive mechanism of anticipatory behavioral control which was introduced in cognitive psychology by Hoffmann (1993). They can learn in deterministic multi-step environments. A stepwise introduction to ACS is given. We start with the basic algorithm and apply it in simple "woods" environments. It will be shown that this algorithm can only learn in a special kind of deterministic multi-step environments. Two extensions are discussed. The first one enables an ACS to learn in any deterministic multi-step environment. The second one allows an ACS to deal with a special kind of non-Markov state.
In Lanzi, Stolzmann, & Wilson (Eds.), Learning Classifier Systems - From Foundations to Applications. LNAI 1813, Heidelberg: Springer-Verlag.
generated: 24 February 2000; last update: 24 February 2000 / WST