This book is probably best summarized as providing a principled foundation for Learning Classifier Systems. Something is happening in LCS, and particularly XCS and its variants that clearly often produces good results. Jan Drug- itsch wishes to understand this from a broader machine learning perspective and thereby perhaps to improve the systems. His approach centers on choosing a statistical definition – derived from machine learning – of “a good set of classifiers”, based on a model according to which such a set represents the data. For an illustration of this approach, he designs the model to be close to XCS, and tests it by evolving a set of classifiers using that definition as a—fitness criterion, seeing if the set provides a good solution to two different function approximation problems. It appears to, meaning that in some sense his definition of “good set of classifiers” (also, in his terms, a good model structure) captures the essence, in machine learning terms, of what XCS is doing. In the process of designing the model, the author describes its components and their training in clear detail and links it to currently used LCS, giving rise to recommendations for how those LCS can directly gain from the design of the model and its probabilistic formulation. The seeming complexity of evaluating the quality of a set of classifiers is alleviated by giving analgorithmic description of how to do it, which is carried out via a simple Pittsburgh-style LCS.