In this paper we introduce an extension of the Probably Approximately Correct (PAC) learning model to study the problem of learning inclusion hierarchies of concepts (sometimes called is-a hierarchies) from random examples. Using only the hypothesis representations output over many different runs of a learning algorithm, we wish to reconstruct the partial order (with respect to generality) amon...