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A Connectionist Model of Spatial Knowledge Acquisition in a Virtual Environment
Date Issued
2003-06-22
Date Available
2013-07-09T12:14:38Z
Abstract
This paper proposes the use of neural networks as a tool for studying
navigation within virtual worlds. Results indicate that network learned to
predict the next step for a given trajectory, acquiring also basic spatial
knowledge in terms of landmarks and configuration of spatial layout. In
addition, the network built a spatial representation of the virtual world, e.g.
cognitive-like map, which preserves the topology but lacks metric accuracy.
The benefits of this approach and the possibility of extending the methodology
to the study of navigation in Human Computer Interaction are discussed.
navigation within virtual worlds. Results indicate that network learned to
predict the next step for a given trajectory, acquiring also basic spatial
knowledge in terms of landmarks and configuration of spatial layout. In
addition, the network built a spatial representation of the virtual world, e.g.
cognitive-like map, which preserves the topology but lacks metric accuracy.
The benefits of this approach and the possibility of extending the methodology
to the study of navigation in Human Computer Interaction are discussed.
Type of Material
Conference Publication
Subjects
Language
English
Status of Item
Peer reviewed
Journal
Proceedings of MLIRUM'03 Second Workshop on Machine Learning, Information Retrieval and User Modelling, at 9th International Conference Conference on User Modelling, June 22nd-26th, 2003.
Conference Details
MLIRUM'03, Second Workshop on Machine Learning, Information Retrieval and User Modelling, at 9th International Conference on User Modelling, June 22nd-26th, Pittsburgh, PA, USA
This item is made available under a Creative Commons License
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P183-Sas,O'Hare,Reilly-03.pdf
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