Expressing and Exploiting the Common Subgoal Structure of Classical Planning Domains Using Sketches: Extended Version
This work addresses a bottleneck in automated planning for AI systems by providing a compact way to encode domain-specific knowledge, though it is incremental as it builds on existing sketch-based methods.
The paper tackles the limitations of width-based planning methods like SIW, which fail when goals are not easily serializable or subproblems have high width, by introducing policy sketches to express finer problem decompositions, resulting in the SIW_R algorithm that solves many previously unsolvable planning domains in low polynomial time.
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.