?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Learning+Appropriate+Contexts&rft.creator=Edmonds%2C+Bruce&rft.subject=Cognitive+Psychology&rft.subject=Artificial+Intelligence&rft.subject=Machine+Learning&rft.description=Genetic+Programming+is+extended+so+that+the+solutions+being+evolved+do+so+in+the+context+of++local+domains+within+the+total%0Aproblem+domain.++This+produces+a+situation+where+different+%C2%93species%C2%94+of+solution+develop+to+exploit+different+%C2%93niches%C2%94+of+the%0Aproblem+%C2%96+indicating+exploitable+solutions.++It+is+argued+that+for+context+to+be+fully+learnable+a+further+step+of+abstraction+is%0Anecessary.++Such+contexts+abstracted+from+clusters+of+solution%2Fmodel+domains+make+sense+of+the+problem+of+how+to+identify%0Awhen+it+is+the+content+of+a+model+is+wrong+and+when+it+is+the+context.++Some+principles+of+learning+to+identify+useful+contexts%0Aare+proposed.&rft.publisher=Springer-verlag&rft.contributor=Akman%2C+Varol&rft.contributor=Bouquet%2C+Paolo&rft.contributor=Thomason%2C+Richmond&rft.contributor=Young%2C+Roger&rft.date=2001&rft.type=Conference+Paper&rft.type=PeerReviewed&rft.format=application%2Fpdf&rft.identifier=http%3A%2F%2Fcogprints.org%2F1772%2F1%2Flac.pdf&rft.format=text%2Fhtml&rft.identifier=http%3A%2F%2Fcogprints.org%2F1772%2F5%2Findex.html&rft.identifier=++Edmonds%2C+Bruce++(2001)+Learning+Appropriate+Contexts.++%5BConference+Paper%5D+++++&rft.relation=http%3A%2F%2Fcogprints.org%2F1772%2F