A Learning Classifier Systems Model

Contents

Specifying the model that is formed by a set of classifiers is central to the model-based approach. On one hand it explicitly defines the assumptions that are made about the problem that we want to solve, and on the other hand it determines the training methods that can be used to provide a solution. This chapter gives a conceptual overview over the LCS model, which is turned into a probabilistic formulation in the next chapter.

As specified in Chap. 1, the tasks that LCS are commonly applied to are regression tasks, classification tasks, and sequential decision tasks. The underlying theme of providing solutions to these tasks is to build a model that maps a set of observed inputs to their associated outputs. Taking the generative view, we assume that the observed input/output pairs are the result of a possibly stochastic process that generates an output for each associated input. Thus, the role of the model is to provide a good representation of the data-generating process.