Representing the spatial/kinematic domain and lattice computers -- A solution of the credit assignment problem in the case of learning rectangles -- Learning decision strategies with genetic algorithms -- Background knowledge and declarative bias in inductive concept learning -- Too much information can be too much for learning efficiently -- Some experiments with a learning procedure -- Unions of identifiable classes of total recursive functions -- Learning from multiple sources of inaccurate data -- Strong separation of learning classes -- Desiderata for generalization-to-N algorithms -- The power of probabilism in Popperian FINite learning -- An analysis of various forms of 'jumping to conclusions' -- An inductive inference approach to classification -- Asking questions versus verifiability -- Predictive analogy and cognition -- Learning a class of regular expressions via restricted subset queries -- A unifying approach to monotonic language learning on informant -- Characterization of finite identification -- A model of the 'redescription' process in the context of geometric proportional analogy problems -- Inductive strengthening: The effects of a simple heuristic for restricting hypothesis space search -- On identifying DNA splicing systems from examples.