Takes initial steps toward a theory of generalization and learning in the learning classifier system XCS. We start from Wilson's generalization hypothesis, which states that XCS has an intrinsic tendency to evolve accurate, maximally general classifiers. We analyze the different evolutionary pressures in XCS and derive a simple equation that supports the hypothesis theoretically. The equation is tested with a number of experiments that confirm the model of generalization pressure that we provide. Then, we focus on the conditions, termed "challenges," that must be satisfied for the existence of effective fitness or accuracy pressure in XCS. We derive two equations that suggest how to set the population size and the covering probability so as to ensure the development of fitness pressure. We argue that when the challenges are met, XCS is able to evolve problem solutions reliably. When the challenges are not met, a problem may provide intrinsic fitness guidance or the reward may be biased in such a way that the problem will still be solved. The equations and the influence of intrinsic fitness guidance and biased reward are tested on large Boolean multiplexer problems. The paper is a contribution to understanding how XCS functions and lays the foundation for research on XCS's learning complexity

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Abstract-Computer games are a promising tool to support rehabilitation at home. It is widely recognized that rehabilitation games should (i) be nicely integrated in general-purpose rehabilitation stations, (ii) adhere to the constraints posed by the clinical protocols, (iii) involve movements that are functional to reach the rehabilitation goal, and (iv) adapt to the patients' current status and progress. However, the vast majority of existing rehabilitation games are stand-alone applications (not integrated in a patient station), that rarely adapt to the patients' condition. In this paper, we present the first prototype of the patient rehabilitation station we developed that integrates video games for rehabilitation with methods of computational intelligence both for on-line monitoring the movements' execution during the games and for adapting the gameplay to the patients' status. The station employs a fuzzy system to monitor the exercises execution, on-line, according to the clinical constraints defined by the therapist at configuration time, and to provide direct feedback to the patients. At the same time, it applies real-time adaptation (using the Quest Bayesian adaptive approach) to modify the gameplay according both (i) to the patient current performance and progress and (ii) to the exercise plan specified by the therapist. Finally, we present one of the games available in our patient stations (designed in tight cooperation with therapists) that integrates monitoring functionalities with in-game self-adaptation to provide the best support possible to patients during their routine.