[Paper Review] Ideal Partition of Resources for Metareasoning

Eric Horvitz, John S. Breese|arXiv (Cornell University)|Oct 18, 2021

Multi-Agent Systems and Negotiation11 references17 citations

TL;DR

This paper introduces the metareasoning-partition problem, proposing an ideal allocation of limited computational resources between metareasoning (planning how to solve a problem) and base-level execution (solving the problem itself). By modeling resource trade-offs across problem classes, it demonstrates that optimally partitioning resources maximizes solution value, with key gains observed when metareasoning is constrained to avoid overcommitment.

ABSTRACT

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.

Motivation & Objective

• To address the challenge of allocating limited computational resources between metareasoning and base-level problem solving.
• To identify the ideal trade-off between time spent on planning (metareasoning) and time spent on executing solutions.
• To examine how resource allocation affects solution quality and computational efficiency across different problem classes.
• To formalize the metareasoning-partition problem as a core issue in rational agency and bounded optimality.
• To evaluate the value of metareasoning under functional analyses of resource trade-offs.

Proposed method

• Formalizes the metareasoning-partition problem as a decision-making challenge in bounded rationality.
• Develops prototypical models to simulate time allocation between metareasoning and execution for various problem types.
• Applies functional analysis to evaluate the value of metareasoning under different resource partitions.
• Uses time-based resource allocation as a proxy for computational cost in both metareasoning and execution phases.
• Employs a framework inspired by bounded optimality to assess trade-offs in resource commitment.
• Analyzes the impact of metareasoning duration on solution quality and overall computational value.

Experimental results

Research questions

• RQ1 What is the optimal proportion of resources to allocate to metareasoning versus execution for a given problem class?
• RQ2 How does the value of the solution change as a function of time spent on metareasoning?
• RQ3 In what ways does over-investment in metareasoning reduce overall solution value?
• RQ4 How do different problem types influence the ideal partition of reasoning resources?
• RQ5 What is the functional relationship between metareasoning effort and the quality of the final solution?

Key findings

• Optimal resource partitioning significantly increases the value of computation by balancing metareasoning and execution.
• Excessive metareasoning reduces solution value due to resource overcommitment, highlighting the need for bounded metareasoning.
• The value of metareasoning is context-dependent and diminishes when it consumes too much of the total resource budget.
• Prototypical models show that gains in solution quality are maximized when metareasoning is limited to a fraction of total time.
• The study confirms that rational agency requires explicit control over metareasoning resource consumption to achieve bounded optimality.
• Functional analysis reveals that the ideal partition varies by problem class, with no universal allocation ratio.