WLPlan: Relational Features for Symbolic Planning
This provides a tool for researchers in AI planning to integrate learning and planning modules more efficiently, though it is incremental as it implements existing methods for feature generation.
The authors tackled the challenge of developing scalable learning planners by introducing WLPlan, a C++ package with Python bindings that automatically generates relational features from planning tasks, enabling applications like learning domain control knowledge.
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