Generalized Planning With Procedural Domain Control Knowledge
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Abstract (arXiv)
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.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Oct 2019.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 2; 2024: 3; 2025: 1; 2026: 1.
11 papers.
- 2022Learning Generalized Policies Without Supervision Using GNNscited by 22
- 2022Learning Sketches for Decomposing Planning Problems into Subproblems of Bounded Width: Extended Versioncited by 12
- 2021Learning General Policies from Small Examples Without Supervisioncited by 7
- 2023Generalized Planning as Heuristic Search: A new planning search-space that leverages pointers over objectscited by 6
- 2023General Policies, Subgoal Structure, and Planning Widthcited by 6
- 2022Computing Programs for Generalized Planning as Heuristic Searchcited by 3
- 2025Learning General Policies with Policy Gradient Methodscited by 3
- 2024On Policy Reuse: An Expressive Language for Representing and Executing General Policies that Call Other Policiescited by 1
- 2024Belief-State Query Policies for User-Aligned POMDPscited by 0
- 2024Parallel Strategies for Best-First Generalized Planningcited by 0
- 2026Abstraction Generation for Generalized Planning with Pretrained Large Language Modelscited by 0
- 2026Abstraction Generation for Generalized Planning with Pretrained Large Language Models11 Feb 2026
- 2025Learning General Policies with Policy Gradient Methods22 Dec 2025
- 2024Parallel Strategies for Best-First Generalized Planning31 Jul 2024
- 2024Belief-State Query Policies for User-Aligned POMDPs24 May 2024
- 2024On Policy Reuse: An Expressive Language for Representing and Executing General Policies that Call Other Policies25 Mar 2024
- 2023General Policies, Subgoal Structure, and Planning Width9 Nov 2023
- 2023Generalized Planning as Heuristic Search: A new planning search-space that leverages pointers over objects26 Jan 2023
- 2022Computing Programs for Generalized Planning as Heuristic Search12 May 2022
- 2022Learning Generalized Policies Without Supervision Using GNNs12 May 2022
- 2022Learning Sketches for Decomposing Planning Problems into Subproblems of Bounded Width: Extended Version28 Mar 2022
- 2021Learning General Policies from Small Examples Without Supervision3 Jan 2021