Hypertension and Total-order Forward Decomposition Optimizations
Abstract
Hierarchical Task Network (HTN) planners generate plans using a decomposition process with extra domain knowledge to guide search towards achieving a task. Domain experts develop such domain knowledge through recipes of how to decompose higher level tasks and under what conditions. By leveraging a three-stage compiler design we can support more language descriptions and preprocessing optimizations to exploit such domain knowledge that when chained can greatly improve runtime efficiency. In this paper we evaluate such optimizations with the HyperTensioN HTN planner: winner of the HTN IPC 2020 total-order track.
Fields of Study & Subjects
Formal Methods in Verification
Logic
programming
and type systems
Software Engineering Research
Publication details
- Journal / source
- Proceedings of the 25th International Conference on Autonomous Agents and Multiagent Systems
- Publisher
- International Foundation for Autonomous Agents and Multiagent Systems
- Publication date
- 24 May 2026
- Language
- EN