Reasoning About Beliefs and Actions Under Computational Resource Constraints

TL;DR

This paper explores how reasoning systems can operate effectively under limited computational resources by using approximation, heuristics, and decision theory to balance inference quality and resource costs.

Contribution

It introduces a framework for resource-bounded reasoning that emphasizes approximation strategies, utility-based control, and real-time rationality in computationally constrained environments.

Findings

Decision theory guides resource allocation in reasoning tasks.

Approximation and heuristics improve reasoning efficiency under constraints.

Knowledge of utility structures helps tailor inference to context.

Abstract

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.