Ideal Partition of Resources for Metareasoning
This addresses resource management in AI systems for improved computational efficiency, but it appears incremental as it builds on existing metareasoning research.
The paper tackles the metareasoning-partition problem of optimally allocating resources between planning and executing solutions, analyzing how time allocation affects performance across problem classes and evaluating the value of metareasoning.
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 of resources 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.