首次对计划空间进行量化与定性推理，可高效计数并解释复杂规划。
Counting and Reasoning with Plans
- 将规划问题转化为命题公式，通过知识编译实现计划计数
- 提出'面'概念简化计数难度，揭示操作符的关键作用
- 支持学习剪枝函数与可解释规划，适合需推理的复杂系统
经典规划要求生成达到目标的操作序列。但许多场景需要超越单纯求解计划，而对计划空间进行定量推理仍属空白。核心问题在于计数计划，这关联到计划空间上的条件概率。尽管定性与定量方法在自动化推理其他领域已成熟，本文首次系统研究计划空间的定量与定性推理。重点聚焦多项式有界计划。理论上，分析其复杂性，衍生出丰富的推理模式；由于计数普遍困难，引入更易处理的‘面’（facets）概念，以理解操作符的重要性。实践上，实现规划的定量推理：将规划任务转为命题公式，利用知识编译技术统计不同计划数量。该框架能有效扩展至大规模计划空间，同时支持学习剪枝函数与可解释规划。
原文摘要 · Abstract (English)
Classical planning asks for a sequence of operators reaching a given goal. While the most common case is to compute a plan, many scenarios require more than that. However, quantitative reasoning on the plan space remains mostly unexplored. A fundamental problem is to count plans, which relates to the conditional probability on the plan space. Indeed, qualitative and quantitative approaches are well-established in various other areas of automated reasoning. We present the first study to quantitative and qualitative reasoning on the plan space. In particular, we focus on polynomially bounded plans. On the theoretical side, we study its complexity, which gives rise to rich reasoning modes. Since counting is hard in general, we introduce the easier notion of facets, which enables understanding the significance of operators. On the practical side, we implement quantitative reasoning for planning. Thereby, we transform a planning task into a propositional formula and use knowledge compilation to count different plans. This framework scales well to large plan spaces, while enabling rich reasoning capabilities such as learning pruning functions and explainable planning.
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