Learning classifier systems: a survey Export

@article{citeulike:1454467, abstract = {Learning classifier systems (LCSs) are rule- based systems that automatically build their ruleset. At the origin of Holland\&\#x2019;s work, LCSs were seen as a model of the emergence of cognitive abilities thanks to adaptive mechanisms, particularly evolutionary processes. After a renewal of the field more focused on learning, LCSs are now considered as sequential decision problem-solving systems endowed with a generalization property. Indeed, from a Reinforcement Learning point of view, LCSs can be seen as learning systems building a compact representation of their problem thanks to generalization. More recently, LCSs have proved efficient at solving automatic classification tasks. The aim of the present contribution is to describe the state-of- the-art of LCSs, emphasizing recent developments, and focusing more on the sequential decision domain than on automatic classification.}, address = {Berlin, Heidelberg}, author = {Sigaud, Olivier and Wilson, Stewart W.}, citeulike-article-id = {1454467}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1265581.1265586}, citeulike-linkout-1 = {http://dx.doi.org/10.1007/s00500-007-0164-0}, citeulike-linkout-2 = {http://www.ingentaconnect.com/content/klu/500/2007/00000011/00000011/00000164}, citeulike-linkout-3 = {http://www.springerlink.com/content/8460275h5011n2x8}, day = {1}, doi = {10.1007/s00500-007-0164-0}, issn = {1432-7643}, journal = {Soft Comput.}, keywords = {ai, classifiers, survey}, month = {September}, number = {11}, pages = {1065--1078}, posted-at = {2007-09-25 16:34:34}, priority = {2}, publisher = {Springer-Verlag}, title = {Learning classifier systems: a survey}, url = {http://dx.doi.org/10.1007/s00500-007-0164-0}, volume = {11}, year = {2007} }

TY - JOUR ID - citeulike:1454467 L3 - citeulike-article-id:1454467 N2 - Learning classifier systems (LCSs) are rule- based systems that automatically build their ruleset. At the origin of Holland’s work, LCSs were seen as a model of the emergence of cognitive abilities thanks to adaptive mechanisms, particularly evolutionary processes. After a renewal of the field more focused on learning, LCSs are now considered as sequential decision problem-solving systems endowed with a generalization property. Indeed, from a Reinforcement Learning point of view, LCSs can be seen as learning systems building a compact representation of their problem thanks to generalization. More recently, LCSs have proved efficient at solving automatic classification tasks. The aim of the present contribution is to describe the state-of- the-art of LCSs, emphasizing recent developments, and focusing more on the sequential decision domain than on automatic classification. IS - 11 JF - Soft Comput. SN - 1432-7643 AD - Berlin, Heidelberg EP - 1078 TI - Learning classifier systems: a survey PB - Springer-Verlag VL - 11 SP - 1065 KW - ai KW - classifiers KW - survey AU - Sigaud, Olivier AU - Wilson, Stewart PY - 2007/09/01/ UR - http://dx.doi.org/10.1007/s00500-007-0164-0 ER -