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--- 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. altloc: - http://www.cpm.mmu.ac.uk/cpmrep78.html chapter: ~ commentary: ~ commref: ~ confdates: July 2001 conference: 'Third International and Interdisciplinary Conference on Modelling and Using Context, CONTEXT 2001' confloc: 'Dundee, UK.' contact_email: ~ creators_id: [] creators_name: - family: Edmonds given: Bruce honourific: '' lineage: '' date: 2001 date_type: published datestamp: 2001-08-30 department: ~ dir: disk0/00/00/17/72 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: - family: Akman given: Varol honourific: '' lineage: '' - family: Bouquet given: Paolo honourific: '' lineage: '' - family: Thomason given: Richmond honourific: '' lineage: '' - family: Young given: Roger honourific: '' lineage: '' eprint_status: archive eprintid: 1772 fileinfo: /style/images/fileicons/application_pdf.png;/1772/1/lac.pdf|/style/images/fileicons/text_html.png;/1772/5/index.html full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: pub issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: 'learning, conditions of application, context, evolutionary computing, error' lastmod: 2011-03-11 08:54:46 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: 143-155 pubdom: FALSE publication: ~ publisher: Springer-verlag refereed: TRUE referencetext: |- Aha, D. W. (1989). Incremental, instance-based learning of independent and graded concept descriptions. In Proc. of the 6th Int. Workshop on Machine Learning, 387-391. CA: Morgan Kaufmann. Baum, E. and Durdanovic, I. (2000a). An Evolutionary Post-Production System. http://www.neci.nj.nec.com/homepages/eric/ptech.ps Baum, E. and Durdanovic, I. (2000b). Evolution of Co-operative Problem Solving. http://www.neci.nj.nec.com/homepages/eric/hayek32000.ps Edmonds, B. (1990). The Pragmatic Roots of Context. CONTEXT'99, Trento, Italy, September 1999. Lecture Notes in Artificial Intelligence, 1688:119-132. http://www.cpm.mmu.ac.uk/cpmrep52.html Elman, J. L. (1993). Learning and Development in Neural Networks - The Importance of Starting Small. Cognition, 48:71-99. Gigerenzer, G and Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 104:650-669. Harries, M. B., Sammut, C. and Horn, K. (1998). Extracting Hidden Contexts. Machine Learning, 32:101-126. Holland, J. H. (1992). Adaptation in Natural and Artificial Systems, 2nd Ed., MIT Press, Cambridge, MA. Koza, J. R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press. Moss, S. and Edmonds, B. (1998). Modelling Economic Learning as Modelling. Cybernetics and Systems, 29:215-248. http://www.cpm.mmu.ac.uk/cpmrep03.html Turney, P. D. (1993). Exploiting context when learning to classify. In Proceedings of the European Conference on Machine Learning, ECML-93. 402-407. Vienna: Springer-Verlag. Turney, P. D. (1996). The management of context-sensitive features: A review of strategies. Proceedings of the ICML-96 Workshop on Learning in Context-Sensitive Domains, Bari, Italy, July 3, 60-66. Turney, P. D. and Halasz, M. (1993). Contextual normalisation applied to aircraft gas turbine engine diagnosis. Journal of Applied Intelligence, 3:109-129. Widmer, G. (1997). Tracking Context Changes through Meta-Learning. Machine Learning, 27:259-286. relation_type: [] relation_uri: [] reportno: ~ rev_number: 14 series: ~ source: ~ status_changed: 2007-09-12 16:40:08 subjects: - cog-psy - comp-sci-art-intel - comp-sci-mach-learn succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: Learning Appropriate Contexts type: confpaper userid: 192 volume: 2116