Learning Appropriate Contexts

Edmonds, Bruce (2001) Learning Appropriate Contexts. [Conference Paper]

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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.

Item Type:Conference Paper
Keywords:learning, conditions of application, context, evolutionary computing, error
Subjects:Psychology > Cognitive Psychology
Computer Science > Artificial Intelligence
Computer Science > Machine Learning
ID Code:1772
Deposited By: Edmonds, Dr Bruce
Deposited On:30 Aug 2001
Last Modified:11 Mar 2011 08:54

References in Article

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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


Baum, E. and Durdanovic, I. (2000a). An Evolutionary Post-Production System.

Baum, E. and Durdanovic, I. (2000b). Evolution of Co-operative Problem Solving.

Edmonds, B. (1990). The Pragmatic Roots of Context. CONTEXT'99, Trento, Italy, September 1999. Lecture Notes in Artificial Intelligence, 1688:119-132.

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.

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.


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