图学习让规划更高效，能自动提炼小任务中的通用知识以应对大规模问题。
Graph Learning for Planning: The Story Thus Far and Open Challenges
- 用图结构表示规划任务，捕捉对象间关系与任意数量物体的输入。
- 提出GOOSE框架，从小任务学知识并推广到更大规模规划。
- 指出5个关键开放问题，为未来研究指明方向。
图学习天然适合规划任务，因其能利用规划领域中的关系结构，并接受包含任意数量对象的输入。本文系统研究了图学习在规划中的应用，重点分析了（1）规划任务的图表示方式，（2）图学习架构，以及（3）学习优化公式对学习与规划性能的影响。研究归纳出一个名为GOOSE的框架，该框架通过从少量规划任务中学习领域知识，实现向更大规模任务的扩展。此外，本文还提出并讨论了当前学习用于规划领域中的五个关键开放挑战，认为解决这些问题将推动该领域的技术进步。
原文摘要 · Abstract (English)
Graph learning is naturally well suited for use in planning due to its ability to exploit relational structures exhibited in planning domains and to take as input planning instances with arbitrary number of objects. In this paper, we study the usage of graph learning for planning thus far by studying the theoretical and empirical effects on learning and planning performance of (1) graph representations of planning tasks, (2) graph learning architectures, and (3) optimisation formulations for learning. Our studies accumulate in the GOOSE framework which learns domain knowledge from small planning tasks in order to scale up to much larger planning tasks. In this paper, we also highlight and propose the 5 open challenges in the general Learning for Planning field that we believe need to be addressed for advancing the state-of-the-art.
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