title: Learning Appropriate Contexts creator: Edmonds, Bruce subject: Cognitive Psychology subject: Artificial Intelligence subject: Machine Learning description: 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. publisher: Springer-verlag contributor: Akman, Varol contributor: Bouquet, Paolo contributor: Thomason, Richmond contributor: Young, Roger date: 2001 type: Conference Paper type: PeerReviewed format: application/pdf identifier: http://cogprints.org/1772/1/lac.pdf format: text/html identifier: http://cogprints.org/1772/5/index.html identifier: Edmonds, Bruce (2001) Learning Appropriate Contexts. [Conference Paper] relation: http://cogprints.org/1772/