Ideal Partition of Resources for Metareasoning
Eric Horvitz and John Breese
E. Horvitz and J. Breese. Technical Report KSL-90-26, Knowledge Systems Laboratory, Stanford University, February 1990.
We can achieve significant gains in the value of computation by
metareasoning about the nature or extent of base-level problem solving
before executing a solution. However, resources that are irrevocably
committed to metareasoning are not available for executing a solution.
Thus, it is important to determine the portion ofresources we wish to
apply to metareasoning and control versus to the execution of a
solution plan. Recent research on rational agency has highlighted the
importance of limiting the consumption of resources by metareasoning
machinery. We shall introduce the metareasoning-partition
problem---the problem of ideally apportioning costly reasoning
resources to planning a solution versus applying resource to
executing a solution to a problem. We exercise prototypical
metareasoning-partition models to probe the relationships between time
allocated to metareasoning and to execution for different problem
classes. Finally, we examine the value of metareasoning in the context
of our functional analyses.
Keywords: Bounded optimal systems, bounded optimality, decision-theoretic control of computation, metareasoning, rationality under bounded resources, decision analysis, decision-theoretic inference.