Two Constraint Compilation Methods for Lifted Planning

2025-11-13

AI Summary

To address the instantiation explosion problem caused by qualitative state-trajectory constraints—such as safety, task ordering, and intermediate subgoals—in large-scale planning, this paper proposes two lifting-preserving symbolic constraint compilation techniques. Grounded on a PDDL fragment, our methods operate directly at the lifted level, enabling high-order actions and reasoning over massive object sets without explicit grounding, thereby avoiding combinatorial explosion. Theoretical analysis guarantees correctness and polynomial-time compilation complexity. Empirical evaluation shows that compiled problem instances are 1–3 orders of magnitude smaller than baseline grounded formulations, while solution quality and runtime performance match those of state-of-the-art planners on IPC benchmark domains. Our key contribution is the first fully lifted compilation of trajectory constraints, significantly enhancing scalability and practicality for large-scale, high-arity planning problems.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Programming
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Compiling qualitative state-trajectory constraints without grounding for lifted planning
• Addressing scalability issues with large object counts and high-arity actions
• Enabling efficient constraint handling in large-scale PDDL planning problems

Innovation

• Compiles constraints without grounding for scalability
• Handles qualitative state-trajectory constraints in lifted planning
• Produces succinct planning specifications for large-scale problems