## 2 Literature Review  
Various planning methodologies rooted in HTN planning have emerged to address partially observable environments, including contingent planning and probabilistic contingent planning. Contingent planning is characterized as the uncertainty of the initial state of the world, sensing actions that obtain the factual observation of belief states and the conditional plan for potential contingencies. For instance, CondSHOP2 [23] integrates the conditional forward-chain search technology into the SHOP2 framework [24], substituting it for the former forward-chain planning. It is improved for dealing with uncertain states. Probabilistic contingent planning represents an alternative significant approach for partial observability, incorporating probability reasoning and representation into partial observability. C-SHOP [25], which builds based on SHOP2, likewise involves belief states handling incomplete information about the environment and sensing actions to generate a conditional plan. Its improved successor, PC-SHOP [26], inherits all the characteristics of C-SHOP while introducing probability reasoning regarding the effects of actions. [PERSON] et al. [21] propose a novel methodology utilizing probabilistic HTNs as an effective method for agents to plan under conditions in which their problem-solving knowledge is uncertain, and the environment is non-deterministic. This approach models the environment as a Markov Decision Process (MDP) and employs Earley graphs to bridge HTNs and MDPs. Expanding the review scope to non-hierarchical planning, [PERSON] and [PERSON] [27] propose a planner for probabilistic propositional contingent planning. [PERSON] and [PERSON] [28] compile contingent planning problems with sensing actions into classical planning. [PERSON] et al. [29] propose an online contingent planner with a landmarks-based heuristic. Partially observable contingent planning is developed for the penetration testing problem [30]. [PERSON] [31] develops a heuristic function for Partially Observable Stochastic Contingent Planning. However, it is important to note that these approaches primarily focus on identifying feasible plans. Simultaneously, some studies have investigated HTN-based planning aimed at generating high-quality or even optimal plans. In the HTN planner SHOP2, a branch-and-bound search is used to find the plan with the minimum cost. [PERSON] et al. [32] propose an integrated approach of HTN learning with reinforcement learning to generate high-quality plans by learning the methods and values of them. [PERSON] et al. [33] put forward a genetic algorithm (MGA) to solve the optimum solution searching problem of HTN planning in the above situations. A length-variant chromosome is introduced to represent the possible planning solution in the form of a decomposition tree with dynamic node numbers. [PERSON] and [PERSON] [34] propose employing HTN planning in risk-sensitive planning domains. This approach introduces utility functions that encapsulate the risk preferences associated with compound tasks and adapts a best-first search algorithm to take such utilities into account. HTNPLAN-P [22] generates preferred plans by combining the procedural control knowledge specified by HTNs with rich user preferences, which proposes a branch-and-bound algorithm, together with a set of heuristics that, leveraging the HTN structure, measure progress made towards the satisfaction of preferences. [PERSON] et al. [35] introduce the first SAT-based approach for optimal HTN planning, translating HTN planning into propositional logic to find optimal plans. This process requires bounding the length of the solution rather than the decomposition depth. [PERSON] et al. [36] introduce two novel progression algorithms that avoid unnecessary branching when the problem at hand is partially ordered and show that both are sound and complete.