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

Large language models (LLMs) can learn to perform a wide range of natural language tasks
from just a handful of in-context examples. However, for generating strings from highly …

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 …

Task planning for mobile robots usually relies solely on spatial information and on shallow
domain knowledge, such as labels attached to objects and places. Although spatial …

This paper describes Marvin, a planner that competed in the Fourth International Planning
Competition (IPC 4). Marvin uses action-sequence-memoisation techniques to generate …

The standard PDDL language for classical planning uses several first-order features, such
as schematic actions. Yet, most classical planners ground this first-order representation into …

A number of today's state-of-the-art planners are based on forward state-space search. The
impressive performance can be attributed to progress in computing domain independent …

Many complex domains and even larger problems in simple domains remain challenging in
spite of the recent progress in planning. Besides developing and improving planning …

Abstract Hierarchical Task Network (HTN) planning is an effective yet knowledge intensive
problem-solving technique. It requires humans to encode knowledge in the form of methods …

Some domains, such as real‐time strategy (RTS) games, pose several challenges to
traditional planning and machine learning techniques. In this article, we present a novel on …