用图核方法自动提取规划任务的关联特征，提升学习型规划可扩展性。
WLPlan: Relational Features for Symbolic Planning
- 将规划任务转为图结构，再用图核嵌入为特征向量
- 支持学习领域控制知识与理解规划任务的下游应用
- 兼顾效率与易用性，提供C++核心+Python接口
可扩展的学习型规划研究通常需在不同编程语言间权衡：学习模块常用易用且生态丰富的Python，而规划模块则依赖资源优化的编译语言如C++。为应对构建可扩展学习规划器的需求，我们提出WLPlan——一个带有Python绑定的C++工具包，实现近期有前景的规划任务关系特征自动生成方法。这些特征可用于下游任务，如学习领域控制知识或分析理解规划问题。具体而言，WLPlan具备两项功能：(1) 将规划任务转换为图结构；(2) 通过图核将规划图嵌入为特征向量。代码与安装使用说明详见tinyurl.com/42kymswc。
原文摘要 · Abstract (English)
Scalable learning for planning research generally involves juggling between different programming languages for handling learning and planning modules effectively. Interpreted languages such as Python are commonly used for learning routines due to their ease of use and the abundance of highly maintained learning libraries they exhibit, while compiled languages such as C++ are used for planning routines due to their optimised resource usage. Motivated by the need for tools for developing scalable learning planners, we introduce WLPlan, a C++ package with Python bindings which implements recent promising work for automatically generating relational features of planning tasks. Such features can be used for any downstream routine, such as learning domain control knowledge or probing and understanding planning tasks. More specifically, WLPlan provides functionality for (1) transforming planning tasks into graphs, and (2) embedding planning graphs into feature vectors via graph kernels. The source code and instructions for the installation and usage of WLPlan are available at tinyurl.com/42kymswc
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