将无限动作的数值规划问题转化为可解形式，让传统启发式方法可用。
Subgoaling Relaxation-based Heuristics for Numeric Planning with Infinite Actions
- 抽象控制变量为有界常数效应，简化复杂动作结构。
- 在无限动作场景下仍能有效估计目标距离，提升求解效率。
- 适合研究带参数控制的复杂规划问题的研究者。
带控制参数的数值规划通过引入自由数值变量作为动作参数，导致状态中可能有无限多个可用动作。在此设置下，传统的依赖动作结构的启发式方法不可行。本文识别出一类可解问题——可控、简单的数值问题，并提出一种乐观编译方法，将其转化为简单数值任务。该方法将依赖控制的表达式抽象为有界常数效应和松弛前条件，使子目标启发式能够有效估计目标距离。实验表明，该方法是将传统数值启发式应用于无限动作场景的有效且计算可行的方案，推动了当前技术边界。
原文摘要 · Abstract (English)
Numeric planning with control parameters extends the standard numeric planning model by introducing action parameters as free numeric variables that must be instantiated during planning. This results in a potentially infinite number of applicable actions in a state. In this setting, off-the-shelf numeric heuristics that leverage the action structure are not feasible. In this paper, we identify a tractable subset of these problems--namely, controllable, simple numeric problems--and propose an optimistic compilation approach that transforms them into simple numeric tasks. To do so, we abstract control-dependent expressions into bounded constant effects and relaxed preconditions. The proposed compilation makes it possible to effectively use subgoaling heuristics to estimate goal distance in numeric planning problems involving control parameters. Our results demonstrate that this approach is an effective and computationally feasible way of applying traditional numeric heuristics to settings with an infinite number of possible actions, pushing the boundaries of the current state of the art.
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