Symbolic Numeric Planning with Patterns

TL;DR

This paper introduces Symbolic Pattern Planning, a new encoding method for linear numeric planning problems that improves efficiency and guarantees plan detection where previous methods may fail, demonstrated through competitive experimental results.

Contribution

It presents a novel encoding approach for numeric planning problems that outperforms existing methods in certain scenarios and is validated through empirical evaluation.

Findings

Our encoding has fewer variables and clauses than previous methods.

The proposed planner Patty performs remarkably well on IPC benchmarks.

The new approach guarantees plan detection where other encodings may not.

Abstract

In this paper, we propose a novel approach for solving linear numeric planning problems, called Symbolic Pattern Planning. Given a planning problem $\Pi$, a bound $n$ and a pattern -- defined as an arbitrary sequence of actions -- we encode the problem of finding a plan for $\Pi$ with bound $n$ as a formula with fewer variables and/or clauses than the state-of-the-art rolled-up and relaxed-relaxed-$\exists$ encodings. More importantly, we prove that for any given bound, it is never the case that the latter two encodings allow finding a valid plan while ours does not. On the experimental side, we consider 6 other planning systems -- including the ones which participated in this year's International Planning Competition (IPC) -- and we show that our planner Patty has remarkably good comparative performances on this year's IPC problems.