Leveraging Action Relational Structures for Integrated Learning and Planning
This work addresses a bottleneck in planning for AI systems by improving search efficiency, though it is incremental as it builds on existing planning frameworks.
The paper tackles the problem of integrating learning and planning in classical planning by introducing partial-space search, which leverages the relational structure of actions for earlier pruning, and shows that their planner LazyLifted outperforms state-of-the-art methods on IPC 2023 benchmarks.
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.