When both Grounding and not Grounding are Bad -- A Partially Grounded Encoding of Planning into SAT (Extended Version)
This addresses efficiency issues in AI planning for domains where full grounding is impractical, offering a novel middle-ground approach.
The paper tackles the problem of exponential size blowup in classical planning by introducing a partially grounded SAT encoding that scales linearly with plan length, outperforming state-of-the-art methods in length-optimal planning on hard-to-ground domains.
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.