## 7 Conclusions and Future Work  
Traditional HTN planning takes less account of partial observability and neglects plan quality within partially observable environments, which significantly limits its practical applicability in real-world scenarios. To bridge this research gap, we proposed a probabilistic contingent planner based on HTN, named the HQCP, specifically designed to generate high-quality plans within partially observable environments. We have extended the HTN planning formalisms into partial observability and evaluated them in terms of cost. We developed the heuristic for high-quality plans. A complete planning algorithm has been constructed to reason the whole planning procedure. Finally, two partially observable domains have been selected to verify the performance of the planning approach. The first domain demonstrated that the planner is efficient in coping with partial observability, and the second domain highlighted the ability of high-quality planning in partially observable environments. This paper addresses the challenges posed by partially observable environments and demonstrates the capability to generate high-quality plans under such conditions. This approach is particularly practical in real-world scenarios, which are inherently fraught with uncertainty. For instance, in emergency situations and robotics, numerous uncertainties arise during plan execution, necessitating flexible and robust planning strategies. Moreover, \begin{table}
\begin{tabular}{c c c} \hline \multicolumn{3}{c}{**Plan 2**} \\ \hline
**Actions** & **Start-Time** & **End-Time** \\ \hline (!Observe usable A) & & \\ (!refuel-at A) & 16:35 & 16:55 \\ (!board-passenger 20) & 16:35 & 16:55 \\ (!zoom A B) & 16:55 & 18:30 \\ (!debark-passenger 10) & 18:30 & 18:45 \\ (!board-passenger 30) & 18:30 & 18:45 \\ (!fly B C) & 18:45 & 20:40 \\ (!debark-passenger 40) & 20:40 & 20:55 \\ (!Observe unusable B) & & \\ (!board-passenger 20) & 16:35 & 16:55 \\ (!fly A B) & 16:55 & 19:10 \\ (!debark-passenger 10) & 19:10 & 19:25 \\ (!board-passenger 30) & 19:10 & 19:25 \\ (!Observe usable B) & & \\ (!refuel-at B) & 19:10 & 19:25 \\ (!zoom B C) & 19:25 & 20:55 \\ (!board-passenger 40) & 20:55 & 21:10 \\ (!Observe unusable B) & & \\ NULL & & \\ \hline \end{tabular}
\end{table} 
Table 2: Plan 2 for the extended ZenoTravel problems. this paper optimizes the quality of the plans, making them more suitable for cost-sensitive scenarios such as logistics. By accounting for partial observability and optimizing plan quality, our approach enhances the feasibility and efficiency of planning systems in dynamic and unpredictable settings. Future work will focus on expanding the scope to include a broader range of uncertainties in planning, such as temporal and resource uncertainties. We aim to enhance the representation and reasoning capabilities of our planning systems to better accommodate for various preferences. Additionally, we will explore incorporating machine learning techniques to improve uncertainties, extending the approach to multi-agent systems for collaborative planning, and improving scalability for large-scale problems. Conducting real-world case studies and addressing ethical considerations in AI planning will also be crucial areas of investigation. These efforts will contribute to making HTN planning more robust and applicable to diverse real-world domains. Conceptualization, P.Z. ; methodology, P.Z. ; software, X.L. and K.L. ; validation, Z.L., X.L. and K.K. ; formal analysis, P.Z. and A.Z. ; investigation, D.W.; resources, Z.L. ; data curation, K.K. and X.L. ; writing--original draft preparation, P.Z. ; writing--review and editing, D.W., X.S., and A.Z; visualization, X.L. ; supervision, X.S. ; project administration, D.W.; funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available on request from the corresponding author.