Next: Introduction
Journal of Artificial Intelligence Research 15 (2001), pp.
115-161. Submitted 4/01; published 8/01.
© 2001 AI Access Foundation and Morgan
Kaufmann Publishers.
All rights reserved.
The
GRT Planning System: Backward
Heuristic Construction
in Forward State-Space Planning
Ioannis Refanidis yrefanid@csd.auth.gr
Ioannis Vlahavas vlahavas@csd.auth.gr
Aristotle University
Dept. of Informatics
54006 Thessaloniki, Greece
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.
1. Introduction
2.2 Backward Heuristic Construction
2.3 Related Facts
2.4 The Pre-Processing Algorithm
3. Detecting and Enhancing Incomplete
States
3.1 Detecting Missing Goal Facts
4. Reducing the Size of the Problems
4.1 Eliminating Irrelevant Objects
4.2 Numerical Representation of
Resources
5. Using XOR Constraints to avoid Local
Optimal States
5.3 Decomposing Problems into
Sub-problems using XOR-constraints
7. Related Work
8.1 Measuring the Effectiveness of the
Related Facts
8.2 Using Several Methods to Enhance the
Goals
8.3 Reducing the Size of the Problem
8.4 XOR Constraints
8.5 Best-First and Hill-Climbing
Strategies
8.6 Comparison to other Planners
8.6.1 Logistics
8.6.2 Blocks-world
8.6.3 Freecell
8.6.4 Elevator
8.6.5 Gripper
8.6.6 Hanoi
8.6.7 Puzzle
Next: Introduction
Ioannis Refanidis
14-8-2001