Compiling Temporal Numeric Planning into Discrete PDDL+: Extended Version

2026-03-12

AI Summary

This work addresses the long-standing challenge of efficiently compiling temporal numeric planning problems with continuous actions into PDDL+. Existing approaches struggle to achieve this transformation while preserving semantics and maintaining tractable model size. The paper proposes a practical polynomial-time compilation method that, under the mild assumption that actions are non-self-overlapping, exactly translates such problems into discrete PDDL+ models. The resulting encoding fully preserves the original semantics and incurs only a constant-factor increase in plan length. To the best of our knowledge, this is the first compilation technique that simultaneously guarantees semantic fidelity, polynomial scalability, and applicability to general temporal numeric planning, thereby filling a significant gap in the literature. Empirical evaluation demonstrates that the proposed compilation substantially enhances the feasibility of solving complex temporal numeric planning problems using existing PDDL+ planners.

Technology Category

• Planning, Routing, and Scheduling: Temporal Planning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Since the introduction of the PDDL+ modeling language, it was known that temporal planning with durative actions (as in PDDL 2.1) could be compiled into PDDL+. However, no practical compilation was presented in the literature ever since. We present a practical compilation from temporal planning with durative actions into PDDL+, fully capturing the semantics and only assuming the non-self-overlapping of actions. Our compilation is polynomial, retains the plan length up to a constant factor and is experimentally shown to be of practical relevance for hard temporal numeric problems.

Problem

• temporal planning
• durative actions
• PDDL+
• numeric planning
• compilation

Innovation

• temporal planning
• durative actions
• PDDL+
• compilation
• numeric planning