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Abstract

Scaling up the complexity of a language game brings about a towering scale-up in the uncer- tainty the agents are faced with when acquiring (lexical) form-meaning associations. The two most prominent assumptions influencing the uncertainty in models on word meaning concern (1) meaning transfer and (2) whether a form can be associated with only one part of meaning or any subset of parts of meaning. If meaning has internal structure (e.g. sets of attributes) this second assumption amounts to whether a form can be associated with only one attribute, giving rise to linear uncertainty, or any subset of attributes, resulting in exponential uncertainty. We first present a short overview of different models that each tried to tackle at least one of these assumptions. We propose a new model borrowing ideas from many of these models that can handle the exponential increase in uncertainty when removing both assumptions and allows scaling towards very large meaning spaces (i.e. worlds).

@inproceedings{wellens08evolang7th,
  author={Pieter Wellens},
  title={Coping with Combinatorial Uncertainty in Word Learning: A Flexible Usage-Based Model},
  year={2008},
  month={March},
  pages={370-377},
  editor={A. D. M. Smith and K. Smith and R. Ferrer-i-Cancho},
  publisher={World Scientific},
  booktitle={Proceedings of the 7th International Conference on the Evolution of Language},
  url={http://groups.lis.illinois.edu/amag/langev/paper/wellens08evolang7th.html}
}