Settable Systems: An Extension of Pearl's Causal Model with Optimization, Equilibrium, and Learning

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Summary

The settable systems framework is offered as an extension of the Pearl Causal Model that permits causal discourse in systems embodying optimization, equilibrium, and learning and may prove generally useful for machine learning.

Type
article
Published
2009-12-01
Cited by
68
References
44

Keywords

Pearl, Computer science, Artificial intelligence, Curse of dimensionality, Extension (predicate logic)

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