Abstract
This paper describes a novel methodology for meta-level control of approximate reasoning. We show that approximate reasoning performed by anytime algorithms offers a simple means by which an intelligent system can trade-off decision quality for deliberation cost. The model exploits probabilistic knowledge about the environment and about the performance of each component in order to optimally manage computational resources. An off-line knowledge compilation technique and a run-time monitoring process guarantee that the system's performance is maximized. The paper concludes with a summary of two applications.
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© 1994 Springer-Verlag Berlin Heidelberg
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Zilberstein, S. (1994). Meta-level control of approximate reasoning: A decision theoretic approach. In: Raś, Z.W., Zemankova, M. (eds) Methodologies for Intelligent Systems. ISMIS 1994. Lecture Notes in Computer Science, vol 869. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58495-1_12
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DOI: https://doi.org/10.1007/3-540-58495-1_12
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Online ISBN: 978-3-540-49010-4
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