Robust Identification in the Limit from Incomplete Positive Data

Intuitively, a learning algorithm is robust if it can succeed despite adverse conditions. We examine conditions under which learning algorithms for classes of formal languages are able to succeed when the data presentations are systematically incomplete; that is, when certain kinds of examples are systematically absent. One motivation comes from linguistics, where the phonotactic pattern of a language may be understood as the intersection of formal languages, each of which formalizes a distinct linguistic generalization. We examine under what conditions these generalizations can be learned when the only data available to a learner belongs to their intersection. In particular, we provide three formal definitions of robustness in the identification in the limit from positive data paradigm, and several theorems which describe the kinds of classes of formal languages which are, and are not, robustly learnable in the relevant sense. We relate these results to classes relevant to natural language phonology