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Abstract

A set of simulations are presented that investigate generalization in languages evolved for mobile robots. The mobile robot platform is RatSLAM, a model for Simultaneous Localization and Mapping based on rodent hippocampus that uses visual and odometric information to build up a map of the explored environment. The language agents use information from this system as inputs and are based on simple recurrent neural networks. This paper describes two sets of experiments exploring the nature of generalization in evolved languages. The first study investigated languages evolved from visual inputs and the second study investigated languages evolved from position representations. These studies showed that processing the input prior to the language agent affects the expressivity of the languages and the performance of the agents. Some generalization occurs in these languages. Studies are ongoing to extend these simulations using the simulated world of the robots.

@inproceedings{schulz06AlifeX,
  author={Ruth Schulz and Paul Stockwell and Mark Wakabayashi and Janet Wiles},
  title={Generalization in Languages Evolved for Mobile Robots},
  year={2006},
  pages={486-492},
  editor={Luis M. Rocha and et al.},
  publisher={MIT Press},
  booktitle={Artificial Life X},
  url={http://www.isrl.uiuc.edu/~amag/langev/paper/schulz06AlifeX.html}
}

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