The problem of planning for a robot that operates in environments containing a large
number of objects, taking actions to move itself through the world as well as to change the …

Recent discoveries in automated planning are broadening the scope of planners, from toy
problems to real applications. However, applying automated planners to real-world …

We study few-shot prompting of pretrained large language models (LLMs) towards solving
PDDL planning problems. We are interested in two questions:(1) To what extent can LLMs …

Real-world planning problems often involve hundreds or even thousands of objects,
straining the limits of modern planners. In this work, we address this challenge by learning to …

We present a new approach to learning for planning, where knowledge acquired while
solving a given set of planning problems is used to plan faster in related, but new problem …

Recent advances in reinforcement learning (RL) have led to a growing interest in applying
RL to classical planning domains or applying classical planning methods to some complex …

Robotic motion planning problems are typically solved by constructing a search tree of valid
maneuvers from a start to a goal configuration. Limited onboard computation and real-time …

We investigate the use of machine learning to create effective heuristics for search
algorithms such as IDA⁎ or heuristic-search planners such as FF. Our method aims to …

In this article we review the 2011 International Planning Competition. We give an overview
of the history of the competition, discussing how it has developed since its first edition in …

Generalized planning studies the representation, computation and evaluation of solutions
that are valid for multiple planning instances. These are topics studied since the early days …