Successive Recognition using Local State Models
Per-Erik ForssénSSAB02, Lund
Proceedings SSAB02 Symposium on Image Analysis
Pages 9-12
March 2002
Abstract
This paper describes how a world model for successive recognition can be learned using associative learning. The learned world model consists of a linear mapping that successively updates a high-dimensional system state using performed actions and observed percepts. The actions of the system are learned by rewarding actions that are good at resolving state ambiguities. As a demonstration, the system is used to resolve the localisation problem in a labyrinth.
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Bibtex entry
@InProceedings{pf02, author = {Per-Erik Forss\'en}, title = {Successive Recognition using Local State Models}, booktitle = {Proceedings {SSAB02} {S}ymposium on {I}mage {A}nalysis}, pages = {9--12}, year = {2002}, address = {Lund}, month = {March}, organization = {SSAB} }
Per-Erik Forssén
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Computer Vision Laboratory
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