Behaviorally Correct Learning from Informants

In inductive inference, we investigate the learnability of classes of formal languages. We are interested in what classes of languages are learnable in certain learning settings. A class of languages is learnable, if there is a learner that can identify all of its languages and satisfies the constraints of the learning setting. To identify a language, a learner is presented with information about this very language. When learning from informants, this information consists of examples for numbers that are, and numbers that are not included in the target language. As more and more examples are presented, the learner outputs a hypothesis sequence. To satisfy behaviorally correct identification, this hypothesis sequence must eventually only list correct labels for the target language. In this thesis, we compare the effects of a number of semantic learning restrictions on the learning capabilities for behaviorally correct learning from informants.