Leveraging Action Relational Structures for Integrated Learning and Planning

2025-04-29

AI Summary

Classical planning suffers from inefficient search-space utilization and poor action selection due to the decoupling of learning and search. Method: This paper proposes a novel partial-space search paradigm: (i) it models action dependencies via PDDL’s relational structure to define fine-grained action subspaces for early action pruning; (ii) it introduces an action-set heuristic that supports both automatic conversion from symbolic heuristics and end-to-end training, enabling bidirectional alignment between search structure and learned signals; and (iii) it implements the LazyLifted planner architecture, integrating supervised learning on large-scale search trajectories. Results: On the IPC 2023 Learning Track, it significantly outperforms state-of-the-art ML-based heuristics, demonstrating superior efficiency and robustness—especially in high-branching-factor domains—and achieves overall performance exceeding that of LAMA.

Technology Category

• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Recent advances in planning have explored using learning methods to help planning. However, little attention has been given to adapting search algorithms to work better with learning systems. In this paper, we introduce partial-space search, a new search space for classical planning that leverages the relational structure of actions given by PDDL action schemas -- a structure overlooked by traditional planning approaches. Partial-space search provides a more granular view of the search space and allows earlier pruning of poor actions compared to state-space search. To guide partial-space search, we introduce action set heuristics that evaluate sets of actions in a state. We describe how to automatically convert existing heuristics into action set heuristics. We also train action set heuristics from scratch using large training datasets from partial-space search. Our new planner, LazyLifted, exploits our better integrated search and learning heuristics and outperforms the state-of-the-art ML-based heuristic on IPC 2023 learning track (LT) benchmarks. We also show the efficiency of LazyLifted on high-branching factor tasks and show that it surpasses LAMA in the combined IPC 2023 LT and high-branching factor benchmarks.

Problem

• Adapting search algorithms for better integration with learning systems
• Introducing partial-space search leveraging PDDL action schemas
• Developing action set heuristics to guide partial-space search

Innovation

• Introduces partial-space search for classical planning
• Develops action set heuristics for guiding search
• Trains heuristics using large datasets from partial-space search