Width-based planning methods deal with conjunctive goals by decomposing problems into subproblems of low width. Algorithms like SIW thus fail when the goal is not easily serializable in this way or when some of the subproblems have a high width. In this work, we address these limitations by using a simple but powerful language for expressing finer problem decompositions introduced recently by Bonet and Geffner, called policy sketches. A policy sketch over a set of Boolean and numerical features is a set of sketch rules that express how the values of these features are supposed to change. Like general policies, policy sketches are domain general, but unlike policies, the changes captured by sketch rules do not need to be achieved in a single step. We show that many planning domains that cannot be solved by SIW are provably solvable in low polynomial time with the SIW_R algorithm, the version of SIW that employs user-provided policy sketches. Policy sketches are thus shown to be a powerful language for expressing domain-specific knowledge in a simple and compact way and a convenient alternative to languages such as HTNs or temporal logics. Furthermore, they make it easy to express general problem decompositions and prove key properties of them like their width and complexity.
翻译:以 Width 为基础的规划方法通过将问题分解成低宽度的子问题来处理共生目标。 SIW 这样的解算方法将问题分解成低宽度的子问题。 当目标不易以这种方式连序或某些子问题宽度高时, SIW 这样的解算方法就会失败。 在这项工作中, 我们用简单而有力的语言来表达最近由Bonet 和 Geffner 推出的细微问题分解, 称为政策草图。 一套布林和数字特征的政策草图是一套描述这些特征的价值如何变化的草图规则。 与一般政策一样, 政策草图是通用的, 但与政策草图不同, 草图所捕的修改不需要单一步骤实现。 我们表明, 许多无法通过 SIW 和 Geconomical 解析出来的计划领域, 在使用 SIW_ R 运算法的低调时, 和 SIW 的版本使用用户提供的政策草图。 因此, 政策草图被证明是表达具体域知识的有力语言的有力语言, 其简单、 和宽度问题, 被证明为 Htrolal 的简单化为一种简单、 的简单和最易的逻辑。