Surrogate Search As a Way to Combat Harmful Effects of Ill-behaved Evaluation Functions

TL;DR

This paper identifies the problem of ill-behaved cost-based evaluation functions in planning due to cost variance and proposes surrogate search methods using size-based evaluation functions to improve search robustness and efficiency.

Contribution

It introduces surrogate search with size-based evaluation functions as a novel approach to address issues caused by cost variance in planning.

Findings

Size-based evaluation functions are resistant to cost variance.

Surrogate search improves planning efficiency and robustness.

Cost-sensitive size evaluation balances quality and speed.

Abstract

Recently, several researchers have found that cost-based satisficing search with A* often runs into problems. Although some "work arounds" have been proposed to ameliorate the problem, there has been little concerted effort to pinpoint its origin. In this paper, we argue that the origins of this problem can be traced back to the fact that most planners that try to optimize cost also use cost-based evaluation functions (i.e., f(n) is a cost estimate). We show that cost-based evaluation functions become ill-behaved whenever there is a wide variance in action costs; something that is all too common in planning domains. The general solution to this malady is what we call a surrogatesearch, where a surrogate evaluation function that doesn't directly track the cost objective, and is resistant to cost-variance, is used. We will discuss some compelling choices for surrogate evaluation functions that are based on size rather that cost. Of particular practical interest is a cost-sensitive version of size-based evaluation function -- where the heuristic estimates the size of cheap paths, as it provides attractive quality vs. speed tradeoffs