Toward learning the causal layer of the Spatial Semantic Hierarchy using SOMs
Jefferson Provost, Patrick Beeson, and Benjamin J. Kuipers. Toward learning the causal layer of the Spatial Semantic Hierarchy using SOMs. In Symposium on Learning Grounded Representations, AAAI Spring Symposium Series, Stanford, CA, March 2001. AAAI Technical Report SS-01-05.
Abstract
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.
BibTeX
@InProceedings{Provost-sss-01,
author = {Jefferson Provost and Patrick Beeson and Benjamin
J. Kuipers},
title = {Toward learning the causal layer of the {Spatial
Semantic Hierarchy} using {SOMs}},
booktitle = {Symposium on Learning Grounded Representations},
year = 2001,
series = {AAAI Spring Symposium Series},
address = {Stanford, CA},
month = {March},
note = {AAAI Technical Report SS-01-05.},
abstract = {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.},
bib2html_pubtype ={Workshop},
bib2html_rescat ={Foundational Learning},
}