creators_name: Edmonds, Bruce
editors_name: Akman, Varol
editors_name: Bouquet, Paolo
editors_name: Thomason, Richmond
editors_name: Young, Roger
type: confpaper
datestamp: 2001-08-30
lastmod: 2011-03-11 08:54:46
metadata_visibility: show
title: Learning Appropriate Contexts
ispublished: pub
subjects: cog-psy
subjects: comp-sci-art-intel
subjects: comp-sci-mach-learn
full_text_status: public
keywords: learning, conditions of application, context, evolutionary computing, error
abstract: Genetic Programming is extended so that the solutions being evolved do so in the context of  local domains within the total
problem domain.  This produces a situation where different “species” of solution develop to exploit different “niches” of the
problem – indicating exploitable solutions.  It is argued that for context to be fully learnable a further step of abstraction is
necessary.  Such contexts abstracted from clusters of solution/model domains make sense of the problem of how to identify
when it is the content of a model is wrong and when it is the context.  Some principles of learning to identify useful contexts
are proposed.
date: 2001
date_type: published
volume: 2116
publisher: Springer-verlag
pagerange: 143-155
refereed: TRUE
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citation:   Edmonds, Bruce  (2001) Learning Appropriate Contexts.  [Conference Paper]     
document_url: http://cogprints.org/1772/1/lac.pdf
document_url: http://cogprints.org/1772/5/index.html