Computing Plan-Length Bounds Using Lengths of Longest Paths

TL;DR

This paper introduces a method to precisely compute the longest simple path in factored state spaces, providing tighter bounds that enhance SAT-based planning despite the problem's NEXP-Hard complexity.

Contribution

The authors present a novel approach for exact longest path computation in factored state spaces, improving upper-bound quality for planning applications.

Findings

Computed bounds are significantly tighter than previous methods.

The method improves SAT-based planning efficiency.

Bounds are practically useful despite NEXP-Hard complexity.

Abstract

We devise a method to exactly compute the length of the longest simple path in factored state spaces, like state spaces encountered in classical planning. Although the complexity of this problem is NEXP-Hard, we show that our method can be used to compute practically useful upper-bounds on lengths of plans. We show that the computed upper-bounds are significantly (in many cases, orders of magnitude) better than bounds produced by previous bounding techniques and that they can be used to improve the SAT-based planning.