The GRT Planning System: Backward Heuristic Construction in Forward State-Space Planning

TL;DR

GRT is a domain-independent heuristic planning system that combines backward fact-distance estimation with forward search, improving efficiency and overcoming local optima through domain axioms, demonstrating competitive performance.

Contribution

It introduces a novel backward heuristic estimation in preprocessing combined with forward search, and techniques to improve efficiency and handle local optima in planning.

Findings

GRT is among the fastest planners in various domains.

Backward heuristic estimation improves search guidance.

Decomposition via domain axioms enhances problem-solving.

Abstract

This paper presents GRT, a domain-independent heuristic planning system for STRIPS worlds. GRT solves problems in two phases. In the pre-processing phase, it estimates the distance between each fact and the goals of the problem, in a backward direction. Then, in the search phase, these estimates are used in order to further estimate the distance between each intermediate state and the goals, guiding so the search process in a forward direction and on a best-first basis. The paper presents the benefits from the adoption of opposite directions between the preprocessing and the search phases, discusses some difficulties that arise in the pre-processing phase and introduces techniques to cope with them. Moreover, it presents several methods of improving the efficiency of the heuristic, by enriching the representation and by reducing the size of the problem. Finally, a method of overcoming local optimal states, based on domain axioms, is proposed. According to it, difficult problems are decomposed into easier sub-problems that have to be solved sequentially. The performance results from various domains, including those of the recent planning competitions, show that GRT is among the fastest planners.