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

arXiv: https://arxiv.org/abs/2511.11095 · HTML: https://mlsanity.com/paper/2511.11095/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2025.

## Built on by (most cited first, 1 of 1)

- [Logical Regression for Planning with Axioms](https://mlsanity.com/paper/2607.21414/) (2026)

## Abstract (arXiv)

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 textitCondition rightarrow textitActions 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.