Reasoning about Beliefs and Actions under Computational Resource Constraints

Eric Horvitz

Medical Computer Science Group
Stanford University
Stanford, CA 94305

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Although many investigators affirm a desire to build reasoning systems that behave consistently with the axiomatic basis defined by probability theory and utility theory, limited resources for engineering and computation can make a complete normative analysis impossible. We attempt to move discussion beyond the debate over the scope of problems that can be handled effectively to cases where it is clear that there are insufficient computational resources to perform an analysis deemed as complete. Under these conditions, we stress the importance of considering the expected costs and benefits of applying alternative approximation procedures and heuristics for computation and knowledge acquisition. We discuss how knowledge about the structure of user utility can be used to control value tradeoffs for tailoring inference to alternative contexts. We address the notion of real-time rationality, focusing on the application of knowledge about the expected timewise-refinement abilities of reasoning strategies to balance the benefits of additional computation with the costs of acting with a partial result. We discuss the benefits of applying decision theory to control the solution of difficult problems given limitations and uncertainty in reasoning resources.

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

Appeared originally in the Proceedings of the Third Workshop on Uncertainty in Artificial Intelligence, Seattle WA, pp. 429-444. July 1987. AAAI and Association for Uncertainty in Artificial Intelligence, Mountain View, CA.

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