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。

基于 HTN 的概率性情境规划实现高质量方案

Probabilistic contingent planning based on HTN for high-quality plans

The difficulty of deterministic planning increases exponentially with
search-tree depth. Black-box planning presents an even greater challenge, since
planners must operate without an explicit model of the domain. Heuristics can
make search more efficient, but goal-aware heuristics for black-box planning
usually rely on goal counting, which is often quite uninformative. In this
work, we show how to overcome this limitation by discovering macro-actions that
make the goal-count heuristic more accurate. Our approach searches for
macro-actions with focused effects (i.e. macros that modify only a small number
of state variables), which align well with the assumptions made by the
goal-count heuristic. Focused macros dramatically improve black-box planning
efficiency across a wide range of planning domains, sometimes beating even
state-of-the-art planners with access to a full domain model.

本文介绍了一种通过发现具有聚焦效应的宏操作，使目标计数启发式方法更准确的方法，大大提高了黑盒计划的效率。