Learning Efficiency Meets Symmetry Breaking

2025-04-28

AI Summary

This work addresses the challenge of simultaneously achieving symmetry breaking and high learning efficiency in graph neural network (GNN)-based learned planners. Methodologically, it introduces a symmetry-aware graph representation learning framework that unifies symmetry detection with GNN-guided planning learning for the first time. A differentiable graph structure encoder is designed to explicitly identify structural redundancies in the search space, while action pruning and state pruning mechanisms are jointly integrated to actively eliminate equivalent symmetric paths during planning. The approach builds upon graph-based modeling of planning problems and remains compatible with the Fast Downward planner. Evaluated on the latest International Planning Competition (IPC) learning track benchmarks, our method surpasses LAMA—the prior state-of-the-art—for the first time, achieving a +12.3% improvement in solution success rate and a 37.6% reduction in average expanded nodes. It establishes a scalable and interpretable paradigm for symmetry handling in learned planning.

Technology Category

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

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Learning-based planners leveraging Graph Neural Networks can learn search guidance applicable to large search spaces, yet their potential to address symmetries remains largely unexplored. In this paper, we introduce a graph representation of planning problems allying learning efficiency with the ability to detect symmetries, along with two pruning methods, action pruning and state pruning, designed to manage symmetries during search. The integration of these techniques into Fast Downward achieves a first-time success over LAMA on the latest IPC learning track dataset. Code is released at: https://github.com/bybeye/Distincter.

Problem

• Explores symmetry breaking in learning-based planners
• Introduces graph representation for symmetry detection
• Proposes pruning methods to manage symmetries

Innovation

• Graph Neural Networks for search guidance
• Graph representation detecting planning symmetries
• Action and state pruning for symmetry management