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Learning Control of Production Systems

Stolfo, Salvatore

One of the central problems in Artificial Intelligence is that of designing appropriate and efficient mechanisms for representing and learning real-world knowledge. We show that it is feasible to infer the control information from an analysis of traces of the successful executions of the production system program provided by a human trainer, thereby creating a system capable of improving its performance by experience.

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More About This Work

Academic Units
Computer Science
Publisher
Department of Computer Science, Columbia University
Series
Columbia University Computer Science Technical Reports, CUCS-006-79
Published Here
August 30, 2011
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