Furthermore, in partially observable environments, the presence of uncertain states that remain unknown until they are directly sensed introduces significant variability in the quality of generated plans. Merely ensuring the feasibility of a plan is insufficient to meet the demands of the stringent requirements of such dynamic and unpredictable settings. It is imperative to proactively evaluate and consider the quality of a plan prior to its execution, anticipating potential uncertainties and their impact on performance. However, due to incomplete information, solely optimizing for an optimal plan may not be feasible during real-world execution in partially observable environments. The challenge of incomplete information complicates the pursuit of an optimal plan [6]. The complexity and uncertainty inherent in these settings necessitate a nuanced approach to planning. A high-quality plan in a partially observable environment means constructing a plan that includes multiple branches, each designed to address different possible observations and cope effectively with uncertain states as they arise. Moreover, such a plan should aim to minimize expected costs. We take the fire emergency evaluation case as an example. There are several dwellers trapped in a burning building. They may be in some rooms, but their specific locations cannot be predetermined. There are many feasible evacuation plans, but they have different costs. The goal of planning in this domain is to evacuate all the dwellers at the least cost. Therefore, planning under partial observability for high-quality plans is a challenging but valuable problem. Hierarchical task network (HTN) planning [7; 8] is an efficient AI planning approach. It solves complicated planning problems in hierarchical decomposition analogous to the human decision-making process. The main idea is to decompose abstract compound tasks into more specific ones until all tasks are primitive and can be executed directly. Owing to the use of specific domain knowledge, HTN planning has powerful reasoning and good support for large-scale domains [9; 10; 11; 12; 13]. In addition, it has been shown to be more expressive than classical planning representations [7; 14]. HTN planning has been successfully applied in a great deal of real-world projects [15; 16; 17; 18; 19; 20]. Although HTN planning shows great strengths, it faces the limitations seen in partially observable contexts and within high-quality plans' requirement. The studies [21] of HTN planning under partial observability are unable to distinguish between feasible plans in terms of various qualities. They can only obtain a feasible plan to accomplish objectives without considering quality or cost metrics. Several HTN planning approaches [22] for high-quality plans concentrate upon the deterministic domains. However, when deployed in real-world scenarios, those planning approaches are hampered by incomplete observable information, resulting in failures. To the best of our knowledge, HTN planning for high-quality plans under partial observability has not yet been addressed. This significant gap motivates us to develop a planner capable of dealing with this problem. In this paper, we put forward a novel probabilistic contingent planning approach based on HTN, named the High-Quality Contingent Planner (HQCP), designed to generate high-quality plans within the partially observable environment. We empirically evaluate the HQCP in two partially observable domains, predicate and extended ZenoTravel. The experimental results show that the HQCP performs effectively in terms of both solving probabilistic contingent planning problems and searching for high-quality plans under partial observability. Furthermore, the HQCP enhances the efficiency of the search process for identifying high-quality plans. The main contributions of this paper are summarized as follows:  
* We extend the existing HTN planning formalisms to partial observability and evaluate them regarding the cost.