The architecture and components of a given learning classifier system can be quite variable. It is useful to think of an LCS as a machine consisting of several interacting components. Components may be added or removed, or existing components modified/exchanged to suit the demands of a given problem ___domain (like algorithmic building blocks) or to make the algorithm flexible enough to function in many different problem domains. As a result, the LCS paradigm can be flexibly applied to many problem domains that call for [[machine learning]]. The major divisions among LCS implementations are as follows: (1) Michigan-style architecture vs. Pittsburgh-style architecture,<ref>[http://ryanurbanowicz.com/wp-content/uploads/2016/09/Urbanowicz_Browne_2015_Introducing-Rule-Based-Machine-Learning-A-Practical-Guide-GECCO15-CRC-Copy.pdf {{ExplainIntroducing |Rule-Based reasonMachine =Learning: itisA notPractical clearGuide], whatRyan theseJ. architecturesUrbanowicz areand orWill howBrowne, theysee arepp. different|date=January72-73 2020}}for Michigan-style architecture vs. Pittsburgh-style architecture.</ref>, (2) [[reinforcement learning]] vs. [[supervised learning]], (3) incremental learning vs. batch learning, (4) [[Online machine learning|online learning]] vs. [[offline learning]], (5) strength-based fitness vs. accuracy-based fitness, and (6) complete action mapping vs best action mapping. These divisions are not necessarily mutually exclusive. For example, XCS,<ref name=":10" /> the best known and best studied LCS algorithm, is Michigan-style, was designed for reinforcement learning but can also perform supervised learning, applies incremental learning that can be either online or offline, applies accuracy-based fitness, and seeks to generate a complete action mapping.
