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@misc{cogprints1772, volume = {2116}, editor = {Varol Akman and Paolo Bouquet and Richmond Thomason and Roger Young}, title = {Learning Appropriate Contexts}, author = {Bruce Edmonds}, publisher = {Springer-verlag}, year = {2001}, pages = {143--155}, keywords = {learning, conditions of application, context, evolutionary computing, error}, url = {http://cogprints.org/1772/}, 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.} }