A new and original trend in the Learning Classifier System (LCS) framework is focussed on latent learning. These new LCSs call upon classifiers with a [condition], an [action] and an [effect] part. In the LCS framework, the latent learning process is in charge of discovering classifiers which are able to anticipate accurately the consequences of actions under some conditions. Accordingly, this process builds a model of the dynamics of the environment. This paper describes how YACS performs latent learning, and how it is enhanced by a dedicated generalization process which offers an alternative to Genetic Algorithms.