Abstract
In this paper we introduce a model for the simulation of language evolution, which is incorporated in the New Ties project. The New Ties project aims at evolving a cultural society by integrating evolutionary, individual and social learning in large scale multi-agent simulations. The model presented here introduces a novel implementation of language games, which allows agents to communicate in a more natural way than with most other existing implementations of language games. In particular, we propose a hybrid mechanism that combines cross-situational learning techniques with more informed feedback mechanisms. In our study we focus our attention on dealing with referential indeterminacy after joint attention has been established and on whether the current model can deal with larger populations than previous studies involving cross-situational learning. Simulations show that the proposed model can indeed lead to coherent languages in a quasi realistic world environment with larger populations.
This research and the New Ties project is supported by an EC FET grant under contract 003752. We thank all members the New Ties project for their invaluable contributions. Opinions and errors in this manuscript are the authors’ responsibility, they do not necessarily reflect those of the EC or other New Ties members.
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Divina, F., Vogt, P. (2006). A Hybrid Model for Learning Word-Meaning Mappings. In: Vogt, P., Sugita, Y., Tuci, E., Nehaniv, C. (eds) Symbol Grounding and Beyond. EELC 2006. Lecture Notes in Computer Science(), vol 4211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11880172_1
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DOI: https://doi.org/10.1007/11880172_1
Publisher Name: Springer, Berlin, Heidelberg
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