Learning classifier system: Difference between revisions

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</ref> This first system, named '''Cognitive System One (CS-1)''' was conceived as a modeling tool, designed to model a real system (i.e. ''environment'') with unknown underlying dynamics using a population of human readable rules. The goal was for a set of rules to perform [[online machine learning]] to adapt to the environment based on infrequent payoff/reward (i.e. reinforcement learning) and apply these rules to generate a behavior that matched the real system. This early, ambitious implementation was later regarded as overly complex, yielding inconsistent results.<ref name=":1" /><ref name=":3">{{Cite journal|last=Lanzi|first=Pier Luca|date=2008-02-08|title=Learning classifier systems: then and now|url=http://link.springer.com/article/10.1007/s12065-007-0003-3|journal=Evolutionary Intelligence|language=en|volume=1|issue=1|pages=63–82|doi=10.1007/s12065-007-0003-3|issn=1864-5909}}</ref>

Beginning in 1980, [[Kenneth A De Jong|Kenneth Dede Jong]] and his student Stephen Smith took a different approach to rule-based machine learning with '''(LS-1)''', where learning was viewed as an offline optimization process rather than an online adaptation process.<ref>Smith S (1980) A learning system based on genetic adaptive

algorithms. Ph.D. thesis, Department of Computer Science,

University of Pittsburgh