is one of the most successful approaches to but unfortunately, it does not trivially extend to (GP); GP aims to compute algorithmic solutions that are valid for a set of classical planning instances from a given domain, even if these instances differ in their number of objects, the initial and goal configuration of these objects and hence, in the number (and possible values) of the state variables. , as it is implemented by heuristic planners, becomes then impractical for GP. In this paper we adapt the paradigm to the generalization requirements of GP, and present the first native heuristic search approach to GP. First, the paper introduces a new pointer-based solution space for GP that is independent of the number of classical planning instances in a GP problem and the size of those instances (i.e. the number of objects, state variables and their domain sizes). Second, the paper defines an upgraded version of our GP algorithm, called (), that implements a in our pointer-based solution space for GP. Lastly, the paper defines a set of evaluation and heuristic functions for that assess the structural complexity of the candidate GP solutions, as well as their fitness to a given input set of classical planning instances. The computation of these evaluation and heuristic functions does not require grounding states or actions in advance. Therefore our approach can handle large sets of state variables with large numerical domains, e.g. .

中文翻译：

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作为启发式搜索的广义规划：利用对象指针的新规划搜索空间

是最成功的方法之一，但不幸的是，它并没有简单地扩展到（GP）； GP 的目标是计算对给定域中的一组经典规划实例有效的算法解决方案，即使这些实例的对象数量、这些对象的初始和目标配置以及因此的数量（和可能值）不同）的状态变量。由于它是由启发式规划者实施的，因此对于 GP 来说变得不切实际。在本文中，我们使范式适应 GP 的泛化要求，并提出了第一个 GP 的本地启发式搜索方法。首先，本文介绍了一种新的基于指针的 GP 解空间，该空间独立于 GP 问题中经典规划实例的数量以及这些实例的大小（即对象、状态变量及其域大小）的数量。其次，本文定义了我们的 GP 算法的升级版本，称为 ()，它在我们基于指针的 GP 解决方案空间中实现了 a。最后，本文定义了一组评估和启发式函数，用于评估候选 GP 解决方案的结构复杂性，以及它们对给定的经典规划实例输入集的适应性。这些评估和启发式函数的计算不需要预先确定基础状态或动作。因此，我们的方法可以处理具有大数值域的大量状态变量，例如 。