When both Grounding and not Grounding are Bad -- A Partially Grounded Encoding of Planning into SAT (Extended Version)

TL;DR

This paper introduces a partially grounded SAT encoding for planning that maintains lifted actions while grounding predicates, achieving linear scalability and outperforming existing methods on complex domains.

Contribution

It presents a novel SAT encoding that balances grounding and lifting, reducing complexity and improving performance in length-optimal planning.

Findings

Outperforms state-of-the-art in hard-to-ground domains

Scales linearly with plan length

Enables efficient planning for longer, complex problems

Abstract

Classical planning problems are typically defined using lifted first-order representations, which offer compactness and generality. While most planners ground these representations to simplify reasoning, this can cause an exponential blowup in size. Recent approaches instead operate directly on the lifted level to avoid full grounding. We explore a middle ground between fully lifted and fully grounded planning by introducing three SAT encodings that keep actions lifted while partially grounding predicates. Unlike previous SAT encodings, which scale quadratically with plan length, our approach scales linearly, enabling better performance on longer plans. Empirically, our best encoding outperforms the state of the art in length-optimal planning on hard-to-ground domains.