The Metric-FF Planning System: Translating "Ignoring Delete Lists" to Numeric State Variables

TL;DR

This paper extends the 'ignoring delete lists' heuristic to numeric planning by introducing a monotonicity-preserving relaxation, leading to the development of the Metric-FF system, which is highly efficient for linear numeric tasks.

Contribution

It introduces a monotonic relaxation for numeric planning, enabling the extension of FF's heuristic algorithms to linear numeric tasks, improving planning efficiency.

Findings

Metric-FF outperforms other numeric planners in IPC-3.

The relaxation preserves key theoretical properties for monotonic numeric tasks.

Pre-processing can achieve monotonicity in linear numeric tasks.

Abstract

Planning with numeric state variables has been a challenge for many years, and was a part of the 3rd International Planning Competition (IPC-3). Currently one of the most popular and successful algorithmic techniques in STRIPS planning is to guide search by a heuristic function, where the heuristic is based on relaxing the planning task by ignoring the delete lists of the available actions. We present a natural extension of ``ignoring delete lists'' to numeric state variables, preserving the relevant theoretical properties of the STRIPS relaxation under the condition that the numeric task at hand is ``monotonic''. We then identify a subset of the numeric IPC-3 competition language, ``linear tasks'', where monotonicity can be achieved by pre-processing. Based on that, we extend the algorithms used in the heuristic planning system FF to linear tasks. The resulting system Metric-FF is, according to the IPC-3 results which we discuss, one of the two currently most efficient numeric planners.