Probabilistic Contingent Planning Based on Hierarchical Task Network for High-Quality Plans
Peng Zhao, Xiaoyu Liu, Xuqi Su, Di Wu, Zi Li, Kai Kang, Keqin Li, Armando Zhu
Abstract
Deterministic hierarchical task network (HTN) planning assumes that planning evolves along a fully predictable path and neglects the quality of the plan in the partially observable environment. To bridge this research gap, this paper proposes an innovative probabilistic contingent HTN planner, named the High-Quality Contingent Planner (HQCP), designed to generate high-quality plans within partially observable contexts. Our methodology extends conventional HTN planning formalisms to accommodate for partial observability and assesses these extensions based on plan cost. Additionally, we propose a novel heuristic for high-quality plans and develop the integrated planning algorithm. These empirical studies verify the effectiveness and efficiency of the planner both in probabilistic contingent planning and in achieving plans of a high quality.