We formalize the function approximation part, by providing a clear problem definition, a formalization of the LCS function approximation architecture, and a definition of function approximation aim.Expand

Walsh and Ungson ([105]) identify that information within organizations can be considered in terms of “Organizational Memories” ...the scattered fragments of data, interpreted by individuals as information, that exists in many forms (written, social, roles, images, ...) throughout an organization.Expand

The development of the XCS Learning Classifier System [26] has produced a stable implementation, able to consistently identify the accurate and optimally general population of classifiers mapping a given reward landscape.Expand

We formalise the mixing problem, which concerns combining the prediction of independently trained local models to form a global prediction, and provide both analytical and heuristic approaches to solving it.Expand

A formal framework that captures all components of classifier systems, that is, function approximation, reinforcement learning, and classifier replacement, and permits the modelling of them separately and in their interaction.Expand

The development of the XCS Learning Classifier System has produced a robust and stable implementation that performs competitively in direct-reward environments.Expand

The 'Aliasing Problem' within XCS (Wilson, 1995, 1998), first identified by Lanzi (1997), does not only appear whenever the aliased states occur in separate environmental locations but also when they occur consecutively (Barry, 1999).Expand

This paper introduces a sub-class of the aliasing problem termed the 'Consecutive State Problem' and uses the subclass to identify the effects of consecutive state aliasing on the learning of the State × Action × Payoff mapping within XCS.Expand