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- W1520804324 abstract "The Anticipatory Classifier System (ACS) is a learning classifier system (LCS) that uses a learning process derived from psychology, which is called Anticipatory Learning Process (ALP). Besides the well known reward learning in LCS, the ACS is able to learn a model of its environment by using the ALP. The internal model of the environment consists of condition-action-effect rules. A typical question is LCS research is whether the rules are accurate and maximally general, i.e., whether the rules can be applied in a maximum number of situation. Latest research observed that the ACS is not generating accurate, maximally general rules reliably, but sometimes produces over-specialized rules. A genetic algorithm is used to overcome this pressure of over-specialization. This invited paper gives an introduction to the current version of ACS. Applications are not discussed. They can be found in “Anticipatory Classifier Systems: An Overview of Applications” (this volume)." @default.
- W1520804324 created "2016-06-24" @default.
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- W1520804324 date "2001-01-01" @default.
- W1520804324 modified "2023-10-18" @default.
- W1520804324 title "Anticipatory Classifier Systems: An introduction" @default.
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- W1520804324 doi "https://doi.org/10.1063/1.1388713" @default.
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