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[2511.10164] Two Constraint Compilation Methods for Lifted Planning
Summary
This paper presents two innovative constraint compilation methods for lifted planning in AI, addressing scalability issues in existing compilers by avoiding grounding, thus enhancing efficiency in large-scale planning problems.
Why It Matters
The research tackles a significant challenge in AI planning by introducing methods that improve scalability and efficiency, which is crucial for real-world applications requiring complex planning. This advancement can lead to more effective AI systems capable of handling larger datasets and more intricate tasks.
Key Takeaways
- Introduces two methods for constraint compilation without grounding.
- Proves the correctness and outlines worst-case time complexity of the methods.
- Demonstrates efficiency and succinctness in planning specifications compared to traditional compilers.
- Empirical evaluations show competitive performance with state-of-the-art planners.
- Addresses scalability issues in AI planning for real-world applications.
Computer Science > Artificial Intelligence arXiv:2511.10164 (cs) [Submitted on 13 Nov 2025 (v1), last revised 20 Feb 2026 (this version, v2)] Title:Two Constraint Compilation Methods for Lifted Planning Authors:Periklis Mantenoglou, Luigi Bonassi, Enrico Scala, Pedro Zuidberg Dos Martires View a PDF of the paper titled Two Constraint Compilation Methods for Lifted Planning, by Periklis Mantenoglou and 3 other authors View PDF 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 produ...
