Surrogate Search As a Way to Combat Harmful Effects of Ill-behaved Evaluation Functions
This addresses a specific bottleneck in planning domains for researchers and practitioners, but is incremental as it builds on existing work arounds.
The paper tackles the problem of cost-based satisficing search with A* encountering issues due to ill-behaved evaluation functions, particularly when action costs vary widely, and proposes surrogate search using size-based functions as a solution, offering improved quality vs. speed tradeoffs.
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