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.

翻译：动作代价信息对于现实世界中的人工智能规划应用至关重要。近期方法不再仅依赖声明性动作模型，而是采用黑箱式外部动作代价估计器（通常从数据中学习得到），并将其应用于规划阶段。然而，这类估计器计算开销可能较高，且会产生不确定的值。本文提出一种具有动作代价的确定性规划的泛化方法，该方法允许在多个动作代价估计器间进行选择，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——且相应更符合现实——的问题表征。重要的是，该方法使规划者能够限定规划的精确度，从而在减少不必要计算负担的同时提升可靠性，这对于扩展到大规模问题至关重要。我们引入一种搜索算法（泛化 $A^*$）来解决此类规划问题，并提出了额外的算法扩展。除理论保证外，大量实验表明，与替代方法相比，该方法在运行时间上取得了显著节省。