使用具有聚焦效果的宏观动作的高效黑箱规划
Efficient Black-Box Planning Using Macro-Actions with Focused Effects
摘要：
确定性规划的难度随着搜索树的深度呈指数级增加。黑箱规划提出了一个更大的挑战，因为规划者必须在没有明确的领域模型的情况下进行操作。启发式可以使搜索更有效，但黑盒规划的目标感知启发式通常依赖于目标计数，这通常是相当没有信息的。在这项工作中，我们将展示如何克服这一限制，发现宏观行动，使目标计数启发式更准确。我们的方法搜索具有集中效果的宏动作（即仅修改少量状态变量的宏），这些宏动作与目标计数启发式所做的假设非常一致。聚焦宏在广泛的规划领域中显著提高了黑盒规划效率，有时甚至击败了访问完整领域模型的最先进的规划器。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
