  We present a new algorithm to model and investigate the learning process of a
learner mastering a set of grammatical rules from an inconsistent source. The
compelling interest of human language acquisition is that the learning succeeds
in virtually every case, despite the fact that the input data are formally
inadequate to explain the success of learning. Our model explains how a learner
can successfully learn from or even surpass its imperfect source without
possessing any additional biases or constraints about the types of patterns
that exist in the language. We use the data collected by Singleton and Newport
(2004) on the performance of a 7-year boy Simon, who mastered the American Sign
Language (ASL) by learning it from his parents, both of whom were imperfect
speakers of ASL. We show that the algorithm possesses a frequency-boosting
property, whereby the frequency of the most common form of the source is
increased by the learner. We also explain several key features of Simon's ASL.
