Planning Through Stochastic Local Search and Temporal Action Graphs in LPG

TL;DR

This paper introduces techniques for temporal planning using stochastic local search and Temporal Action Graphs in LPG, demonstrating improved speed and quality of solutions in planning competitions.

Contribution

It presents a novel approach combining stochastic local search with Temporal Action Graphs for temporal planning in PDDL2.1 domains, enhancing performance.

Findings

LPG outperforms other planners in speed and solution quality.

Techniques are effective in temporal planning as shown in IPC results.

Temporal Action Graphs guide search efficiently in complex domains.

Abstract

We present some techniques for planning in domains specified with the recent standard language PDDL2.1, supporting 'durative actions' and numerical quantities. These techniques are implemented in LPG, a domain-independent planner that took part in the 3rd International Planning Competition (IPC). LPG is an incremental, any time system producing multi-criteria quality plans. The core of the system is based on a stochastic local search method and on a graph-based representation called 'Temporal Action Graphs' (TA-graphs). This paper focuses on temporal planning, introducing TA-graphs and proposing some techniques to guide the search in LPG using this representation. The experimental results of the 3rd IPC, as well as further results presented in this paper, show that our techniques can be very effective. Often LPG outperforms all other fully-automated planners of the 3rd IPC in terms of speed to derive a solution, or quality of the solutions that can be produced.