规划问题分解为有界宽度子问题的草图学习法：扩展版
Learning Sketches for Decomposing Planning Problems into Subproblems of Bounded Width: Extended Version
摘要：
最近，草图已经被引入作为一种通用语言，用于表示从同一领域中提取的实例的子目标结构。草图是给定特征集上C -> E形式的规则集合，其中C表示布尔条件，E表示定性变化。每个草图规则定义了一个子问题：从满足C的状态到实现E或目标状态所表达的变化的状态。草图可以编码简单的目标序列化、一般策略或有界宽度的分解，这些可以通过SIW算法的SIW_R变体在多项式时间内以greedy方式求解。以前的工作已经表明，在基准领域的草图的计算价值，而听话的，是具有挑战性的领域独立的规划。在这项工作中，我们解决的问题，自动学习草图给定的规划域，目标类的问题，和所需的约束上的草图宽度的一些实例。我们提出了一个逻辑制定的问题，使用ASP求解器Clingo的实施，和实验结果。草图学习器和SIW_R规划器产生了一个独立于领域的规划器，它以清晰明确的形式学习和利用领域结构。
英文摘要：
Recently, sketches have been introduced as a general language for representing the subgoal structure of instances drawn from the same domain. Sketches are collections of rules of the form C -> E over a given set of features where C expresses Boolean conditions and E expresses qualitative changes. Each sketch rule defines a subproblem: going from a state that satisfies C to a state that achieves the change expressed by E or a goal state. Sketches can encode simple goal serializations, general policies, or decompositions of bounded width that can be solved greedily, in polynomial time, by the SIW_R variant of the SIW algorithm. Previous work has shown the computational value of sketches over benchmark domains that, while tractable, are challenging for domain-independent planners. In this work, we address the problem of learning sketches automatically given a planning domain, some instances of the target class of problems, and the desired bound on the sketch width. We present a logical formulation of the problem, an implementation using the ASP solver Clingo, and experimental results. The sketch learner and the SIW_R planner yield a domain-independent planner that learns and exploits domain structure in a crisp and explicit form.
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。
