从少量计划中学习通用目标，提升规划模型对新问题的适应能力。
Revisiting Landmarks: Learning from Previous Plans to Generalize over Problem Instances
- 基于已解问题提取独立于具体对象的状态函数，捕捉重复模式。
- 构建带循环的广义地标图，在相同领域的大规模问题上仍有效。
- 适合需要跨实例泛化的自动规划系统使用。
我们提出一种新框架，用于发现可跨问题实例泛化的广义地标。这些地标从一组已求解实例中学习而来，描述传统地标算法难以处理的规划过程中的中间目标。通过使用独立于特定问题对象的状态函数，广义地标超越了领域谓词的限制，能适用于所有相似对象，从而捕捉重复结构。基于这些函数，我们构建有向广义地标图，刻画地标演进路径，包括重复子计划可能形成的循环。该图可用于设计启发式方法以求解同一领域的新问题。实验表明，仅用少数小型实例学习的广义地标图，对同领域更大规模实例同样有效；若识别出表示重复的循环，则启发式性能显著优于基线。广义地标捕获了可解释且对自动规划器有用的任务域信息，且仅需少量同领域计划即可发现。
原文摘要 · Abstract (English)
We propose a new framework for discovering landmarks that automatically generalize across a domain. These generalized landmarks are learned from a set of solved instances and describe intermediate goals for planning problems where traditional landmark extraction algorithms fall short. Our generalized landmarks extend beyond the predicates of a domain by using state functions that are independent of the objects of a specific problem and apply to all similar objects, thus capturing repetition. Based on these functions, we construct a directed generalized landmark graph that defines the landmark progression, including loop possibilities for repetitive subplans. We show how to use this graph in a heuristic to solve new problem instances of the same domain. Our results show that the generalized landmark graphs learned from a few small instances are also effective for larger instances in the same domain. If a loop that indicates repetition is identified, we see a significant improvement in heuristic performance over the baseline. Generalized landmarks capture domain information that is interpretable and useful to an automated planner. This information can be discovered from a small set of plans for the same domain.
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