Flexible and Approximate Computation through State-Space Reduction

TL;DR

This paper introduces two state-space reduction techniques that improve flexible and approximate decision-making under limited computational resources, enabling better solutions with less computation.

Contribution

It proposes quantitative and structural reduction methods for flexible and approximate computation, enhancing solution quality efficiently under resource constraints.

Findings

Reduction methods outperform existing approaches in solution quality

Methods are effective and computationally efficient

Solution quality improves with successive application of reductions

Abstract

In the real world, insufficient information, limited computation resources, and complex problem structures often force an autonomous agent to make a decision in time less than that required to solve the problem at hand completely. Flexible and approximate computations are two approaches to decision making under limited computation resources. Flexible computation helps an agent to flexibly allocate limited computation resources so that the overall system utility is maximized. Approximate computation enables an agent to find the best satisfactory solution within a deadline. In this paper, we present two state-space reduction methods for flexible and approximate computation: quantitative reduction to deal with inaccurate heuristic information, and structural reduction to handle complex problem structures. These two methods can be applied successively to continuously improve solution quality if more computation is available. Our results show that these reduction methods are effective and efficient, finding better solutions with less computation than some existing well-known methods.