caa a22 4500 605468664 CHVBK 20210128100318.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s00500-014-1357-y doi (NATIONALLICENCE)springer-10.1007/s00500-014-1357-y Learning classifier systems with memory condition to solve non-Markov problems [Elektronische Daten] [Zhaoxiang Zang, Dehua Li, Junying Wang] In the family of learning classifier systems, the classifier system XCS has been successfully used for many applications. However, the standard XCS has no memory mechanism and can only learn optimal policy in Markov environments, but fails in non-Markov ones. In this work, we aim to develop a new classifier system based on XCS to tackle this problem. It adds a memory list with numbered slots to XCS to record input sensation history, and extends only a small number of classifiers with memory conditions. The classifier's memory condition, as a foothold to disambiguate non-Markov states, is used to sense a specified element in the memory list, which makes our system can "jump over” irrelevant or confusing states to get decisive prior information that may be far back in time. Besides, a detection method is employed to recognize non-Markov states in environments, to avoid these states controlling over classifiers' memory conditions. Furthermore, four sets of different complex maze environments have been tested by the proposed method. Experimental results show that our system can overcome the overhead problem often encountered in history-window approaches, and is an effective technique to solve non-Markov environments. Springer-Verlag Berlin Heidelberg, 2014 Learning classifier system nationallicence XCS nationallicence Memory condition nationallicence Aliasing state detection nationallicence Partially observable environments nationallicence Non-Markov problems nationallicence Zang Zhaoxiang College of Computer and Information Technology, China Three Gorges University, 443002, Yichang, Hubei, China aut Li Dehua Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, 430074, Wuhan, Hubei, China aut Wang Junying College of Computer and Information Technology, China Three Gorges University, 443002, Yichang, Hubei, China aut Soft Computing Springer Berlin Heidelberg 19/6(2015-06-01), 1679-1699 1432-7643 19:6<1679 2015 19 500 https://doi.org/10.1007/s00500-014-1357-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1357-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zang Zhaoxiang College of Computer and Information Technology, China Three Gorges University, 443002, Yichang, Hubei, China aut NATIONALLICENCE 700 1- Li Dehua Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, 430074, Wuhan, Hubei, China aut NATIONALLICENCE 700 1- Wang Junying College of Computer and Information Technology, China Three Gorges University, 443002, Yichang, Hubei, China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/6(2015-06-01), 1679-1699 1432-7643 19:6<1679 2015 19 500