Two Constraint Compilation Methods for Lifted Planning

TL;DR

This paper introduces two novel methods for compiling constraints in lifted planning, avoiding grounding to improve scalability and efficiency in large-scale problems.

Contribution

The authors propose two constraint compilation methods that do not require grounding, enhancing scalability for large, complex planning problems.

Findings

Methods are more succinct than grounded counterparts

Approach remains competitive with state-of-the-art planners

Proven correctness and analyzed worst-case complexity

Abstract

We study planning in a fragment of PDDL with qualitative state-trajectory constraints, capturing safety requirements, task ordering conditions, and intermediate sub-goals commonly found in real-world problems. A prominent approach to tackle such problems is to compile their constraints away, leading to a problem that is supported by state-of-the-art planners. Unfortunately, existing compilers do not scale on problems with a large number of objects and high-arity actions, as they necessitate grounding the problem before compilation. To address this issue, we propose two methods for compiling away constraints without grounding, making them suitable for large-scale planning problems. We prove the correctness of our compilers and outline their worst-case time complexity. Moreover, we present a reproducible empirical evaluation on the domains used in the latest International Planning Competition. Our results demonstrate that our methods are efficient and produce planning specifications that are orders of magnitude more succinct than the ones produced by compilers that ground the domain, while remaining competitive when used for planning with a state-of-the-art planner.