基于动态估计动作代价的规划
Planning with Dynamically Estimated Action Costs
AI总结：
针对带动作代价的AI规划中黑盒代价估计器计算成本高、结果不确定的问题，提出可在多估计器间选择的泛化确定性规划框架，设计泛化$A^*$搜索算法，在保证规划精度边界的同时大幅降低运行时间。
摘要：
动作代价信息对于现实世界的AI规划应用至关重要。近期的方法不再仅依赖声明式动作模型，还会使用黑盒外部动作代价估计器（通常从数据中学习得到），在规划阶段执行估计。然而这类方法计算成本高昂，且输出值存在不确定性。本文提出了带动作代价的确定性规划的一种泛化形式，允许在多个动作代价估计器之间进行选择，以在计算时间与有界估计不确定性之间取得平衡。这实现了更丰富、也相应更贴合现实的问题表示。重要的是，它让规划器能够约束规划精度，在提升可靠性的同时减少不必要的计算负担，这对于扩展到大规模问题至关重要。我们提出了一种泛化$A^*$的搜索算法来求解这类规划问题，并给出了额外的算法扩展。除理论保证外，大量实验表明，与替代方案相比，该方法在运行时间上大幅节省。
英文摘要：
Information about action costs is critical for real-world AI planning applications. Rather than rely solely on declarative action models, recent approaches also use black-box external action cost estimators, often learned from data, that are applied during the planning phase. These, however, can be computationally expensive, and produce uncertain values. In this paper we suggest a generalization of deterministic planning with action costs that allows selecting between multiple estimators for action cost, to balance computation time against bounded estimation uncertainty. This enables a much richer -- and correspondingly more realistic -- problem representation. Importantly, it allows planners to bound plan accuracy, thereby increasing reliability, while reducing unnecessary computational burden, which is critical for scaling to large problems. We introduce a search algorithm, generalizing $A^*$, that solves such planning problems, and additional algorithmic extensions. In addition to theoretical guarantees, extensive experiments show considerable savings in runtime compared to alternatives.
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。
