The Spatial Semantic Hierarchy (SSH) is a multi-level representation of the cognitive map used for navigation in largescale space. We propose a method for learning a portion of this representation, specifically, the representation of views in the causal level of the SSH using self-organizing neural networks (SOMs). We describe the criteria that a good view representation should meet, and why SOMs are a promising view representation. Our preliminary experimental results indicate that SOMs show promise as a view representation, though there are still some problems to be resolved.

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In AAAI Spring Symposium Series, Learning Grounded Representations 2001.

@inproceedings{provost:aaaiss01, title={Toward Learning the Causal Layer of the Spatial Semantic Hierarchy using SOMs}, author={Jefferson Provost and Patrick Beeson and Benjamin J. Kuipers}, booktitle={AAAI Spring Symposium Series, Learning Grounded Representations}, url="http://www.cs.utexas.edu/users/ai-lab?provost:aaaiss01", year={2001} }