The growing utilization of planning tools in practical scenarios has sparked
an interest in generating multiple high-quality plans. Consequently, a range of
computational problems under the general umbrella of top-quality planning were
introduced over a short time period, each with its own definition. In this
work, we show that the existing definitions can be unified into one, based on a
dominance relation. The different computational problems, therefore, simply
correspond to different dominance relations. Given the unified definition, we
can now certify the top-quality of the solutions, leveraging existing
certification of unsolvability and optimality. We show that task
transformations found in the existing literature can be employed for the
efficient certification of various top-quality planning problems and propose a
novel transformation to efficiently certify loopless top-quality planning.

这篇论文将现有的定义统一为一种基于支配关系的定义，证明了不同的计算问题实际上对应于不同的支配关系，提出了一种有效证明各种高质量规划问题的方法，并提出了一种新的转化方法以有效证明无循环的高质量规划。

统一和认证高质量规划

Unifying and Certifying Top-Quality Planning

Deterministic planning assumes that the planning evolves along a fully
predictable path, and therefore it loses the practical value in most real
projections. A more realistic view is that planning ought to take into
consideration partial observability beforehand and aim for a more flexible and
robust solution. What is more significant, it is inevitable that the quality of
plan varies dramatically in the partially observable environment. In this paper
we propose a probabilistic contingent Hierarchical Task Network (HTN) planner,
named High-Quality Contingent Planner (HQCP), to generate high-quality plans in
the partially observable environment. The formalisms in HTN planning are
extended into partial observability and are evaluated regarding the cost. Next,
we explore a novel heuristic for high-quality plans and develop the integrated
planning algorithm. Finally, an empirical study verifies the effectiveness and
efficiency of the planner both in probabilistic contingent planning and for
obtaining high-quality plans.

这篇论文介绍了一种名为 High-Quality Contingent Planner（HQCP）的概率性和遗传性任务网络（HTN）规划器，在部分可观察环境中生成高质量的计划。该规划器在 HTN 规划的形式化中引入了部分可观察性，并根据成本进行了评估。同时，提出了一种新的用于高质量计划的启发式方法，并开发了集成规划算法。最后，经验研究验证了该规划器在概率性遗传规划和获取高质量计划方面的有效性和高效性。