Generalized Planning With Procedural Domain Control Knowledge
This work addresses the challenge of scalable generalized planning for AI systems, though it appears incremental as it builds on existing DCK methods by adding procedural capabilities.
The authors tackled the problem of generating generalized plans for multiple planning tasks by introducing procedural Domain Control Knowledge (DCK) with a divide-and-conquer approach, enabling classical planners to compute solutions for complex domains like sorting variable-size lists and DFS traversal of binary trees.
Generalized planning is the task of generating a single solution that is valid for a set of planning problems. In this paper we show how to represent and compute generalized plans using procedural Domain Control Knowledge (DCK). We define a {\it divide and conquer} approach that first generates the procedural DCK solving a set of planning problems representative of certain subtasks and then compile it as callable procedures of the overall generalized planning problem. Our procedure calling mechanism allows nested and recursive procedure calls and is implemented in PDDL so that classical planners can compute and exploit procedural DCK. Experiments show that an off-the-shelf classical planner, using procedural DCK as callable procedures, can compute generalized plans in a wide range of domains including non-trivial ones, such as sorting variable-size lists or DFS traversal of binary trees with variable size.