Unsupervised Learning of Policy Sketches for Classical Planning
||Unsupervised Learning of Policy Sketches for Classical Planning|
||SNIC Small Compute|
||Dominik Drexler <email@example.com>|
||2021-08-09 – 2022-09-01|
In this work, we are trying to learn policy sketches for solving tractable classical planning domains in provably low polynomial time. A policy sketch R decomposes a problem into subproblems. The challenge is to automatically learn a suitable sketch that works on a large class of problems over a common domain. There are certain properties that a sketch has to satisfy such as being well-formed and has bounded and small sketch width in order to be suitable. We use answer set programming for finding suitable sketches automatically.