Evolving Learnable Languages

Traditional theories of child language acquisition center around the existence of a language acquisition device which is speciically tuned for learning a particular class of languages. More recent proposals suggest that language acquisition is assisted by the evolution of languages towards forms that are easily learnable. In this paper, we evolve combinatorial languages which can be learned by a simple recurrent network quickly and from relatively few examples. Additionally, we evolve languages for generalization in diierent \worlds", and for generalization from speciic examples. We nd that languages can be evolved to facilitate diierent forms of impressive generalization for a minimally biased learner. The results provide empirical support for the theory that the language itself, as well as the language environment of a learner, plays a substantial role in learning: that there is far more to language acquisition than the language acquisition device.