Abstract
The Inconsistency Detection Learner (IDL) is an algorithm for language learning that addresses the problem of structural ambiguity. If an overt form is structurally ambiguous, the learner must be capable of inferring which interpretation of the overt form is correct by reference to other overt data of the language. IDL does this by attempting to construct grammars for combinations of interpretations of the overt forms, and discarding those combinations that are inconsistent. The potential of this algorithm for overcoming the combinatorial growth in combinations of interpretations is supported by computational results from an implementation of IDL using an Optimality theoretic system of metrical stress grammars.