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Abstract

Traditional theories of child language acquisition center around the existence of a language acquisition device which is specifically 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 different ``worlds'', and for generalization from specific examples. We find that languages can be evolved to facilitate different 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.

@inproceedings{tonkes00evolvingLearnable,
  author={Bradley Tonkes and Alan Blair and Janet Wiles},
  title={Evolving learnable languages},
  year={2000},
  pages={66-72},
  editor={S. A. Solla and T. K. Leen and K.-R.Muller},
  publisher={MIT Press},
  booktitle={Advances in Neural Information Processing Systems 12, (NIPS*99)},
  url={http://www.isrl.uiuc.edu/~amag/langev/paper/tonkes00evolvingLearnable.html}
}

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