论文标题
学习将计划问题分解为有限宽度子问题的草图：扩展版本
Learning Sketches for Decomposing Planning Problems into Subproblems of Bounded Width: Extended Version
论文作者
论文摘要
最近，素描是作为一种通用语言引入的，用于表示从同一域中绘制的实例的子目标结构。草图是在给定的一组特征上表达布尔条件并表示定性更改的给定功能的c-> e规则的集合。每个草图规则都定义了一个子问题：从满足C的状态到实现E或目标状态表达的变化的状态。草图可以编码可以通过SIW算法的SIW_R变体在多项式时间内贪婪地求解的界宽度的简单目标序列化，一般策略或分解。先前的工作表明，草图对基准域的计算值，尽管可以进行操作，但对于独立于域的计划者来说是具有挑战性的。在这项工作中，我们解决了学习草图的问题自动给定计划域，目标类别类别的某些实例以及在草图宽度上所需的界限。我们提出了问题的逻辑公式，使用ASP求解器克林戈的实现以及实验结果。草图学习者和SIW_R计划者产生了独立于域的计划者，该计划者以清晰和明确的形式学习和利用域结构。
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.
