Satisficing and Optimal Generalised Planning via Goal Regression (Extended Version)

TL;DR

This paper presents a new method for generalised planning that synthesizes plans from training problems using goal regression, resulting in more efficient and effective planning across multiple domains.

Contribution

The paper introduces a simple, goal regression-based approach for generalised planning that guarantees validity and improves planning performance over existing methods.

Findings

Significant improvements in synthesis cost, coverage, and solution quality.

Method guarantees valid generalised plans under formal conditions.

Effective across classical and numeric planning domains.

Abstract

Generalised planning (GP) refers to the task of synthesising programs that solve families of related planning problems. We introduce a novel, yet simple method for GP: given a set of training problems, for each problem, compute an optimal plan for each goal atom in some order, perform goal regression on the resulting plans, and lift the corresponding outputs to obtain a set of first-order $\textit{Condition} \rightarrow \textit{Actions}$ rules. The rules collectively constitute a generalised plan that can be executed as is or alternatively be used to prune the planning search space. We formalise and prove the conditions under which our method is guaranteed to learn valid generalised plans and state space pruning axioms for search. Experiments demonstrate significant improvements over state-of-the-art (generalised) planners with respect to the 3 metrics of synthesis cost, planning coverage, and solution quality on various classical and numeric planning domains.