Discrimination Learning in Production Systems
Besides extending the Bacon research, my early post-dissertation
work focused on adaptive production systems, specifically on an
approach to finding the conditions on rules that I called discrimination
learning. The approach was a variant on an early mechanism that
John Anderson and colleagues developed in ACTF, but I extended the
idea and applied it to new areas. It also bore a close resemblance to
methods developed independently by Pavel Brazdil around the same time.
The basic idea involved storing with each rule the previous situation
(the state of working memory and variable bindings) in which applied
correctly and comparing this with a new situation in which it applied
incorrectly. This comparison produced a set of differences, each of
which led to a candidate rule that was more specific that the one
that had fired. The method constructed all such variants, then used
a strengthening process to collect statistical evidence and resolve
conflicts at performance time. In hindsight, this approach was an
incremental version of the separate-and-conquer methods that now
enjoy some popularity in machine learning.
I incorporated this approach to rule learning into a number of
systems, including the Amber model of syntax acquisition, the Sage
system for learning search heuristics, and the first computational
model of learning on the balance-scale task (done jointly with
Stephanie Sage). The same basic method played a role in early
versions of ACM, a system that automated
cognitive modeling (developed with Stellan Ohlsson and Stephanie
Sage). These systems were implemented within the PRISM architecture,
which supported discrimination as one of its central learning
mechanisms.
Related Publications
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Langley, P. (1987). A general theory of discrimination learning.
In D. Klahr, P. Langley, & R. Neches (Eds.), Production system
models of learning and development. Cambridge, MA: MIT Press.
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Langley, P. (1985).
Learning to search: From weak methods to domain-specific heuristics.
Cognitive Science, 9, 217-260.
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Sage, S., & Langley, P. (1983).
Modeling development on the balance scale task.
Proceedings of the Eighth International Joint Conference on Artificial
Intelligence (pp. 94-96). Karlsruhe, West Germany: Morgan Kaufmann.
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Langley, P. (1983).
Learning search strategies through discrimination.
International Journal of Man-Machine Studies, 18, 513-541.
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Langley, P. (1983).
Learning effective search heuristics.
Proceedings of the Eighth International Joint Conference on Artificial
Intelligence (pp. 419-421). Karlsruhe, West Germany: Morgan Kaufmann.
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Langley, P. (1982).
Language acquisition through error recovery.
Cognition and Brain Theory, 5, 211-255.
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Langley, P. (1982).
A model of early syntactic development.
Proceedings of the 20th Annual Conference of the Society for
Computational Linguistics (pp. 145-151). Toronto, Ontario.
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Langley, P. (1980).
A production system model of first language acquisition.
Proceedings of the Eighth International Conference on Computational
Linguistics (pp. 183-189). Tokyo, Japan.
For more information, send electronic mail to
patrick.w.langley@gmail.com