Abstract
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 [4]. They can learn in deterministic multi-step environments.1 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.
Butz, Goldberg & Stolzmann [2] show that ACS can also learn in deterministic single-step environments with a perceptual causality in its successive states.
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Stolzmann, W. (2000). An Introduction to Anticipatory Classifier Systems. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Learning Classifier Systems. IWLCS 1999. Lecture Notes in Computer Science(), vol 1813. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45027-0_9
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DOI: https://doi.org/10.1007/3-540-45027-0_9
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