Reasoning Under Varying and Uncertain Resource Constraints

Eric Horvitz

Medical Computer Science Group
Stanford University
Stanford, CA 94305

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Abstract:

We describe the use of decision-theory to optimize the value of computation under uncertain and varying resource limitations. The research is motivated by the pursuit of formal models of rational decision making for computational agents, centering on the explicit consideration of preferences and resource availability. We focus here on the importance of identifying the multiattribute structure of partial results generated by approximation methods for making control decisions. Work on simple algorithms and on the control of decision-theoretic inference itself is described.

Keywords: Rationality, bounded resources, metareasoning, decision theory, flexible computation, bounded optimality.

Appeared in: Proceedings of the Seventh National Conference on Artificial Intelligence (AAAI 1988), Minneapolis, Minnesota, 111-116. Morgan Kaufmann 1988.

Author Email: horvitz@camis.stanford.edu