Nudging Automated Planners with Learned User Preferences

Fusun Yaman, Thomas Eskridge, Ron Scott, Li Lin, Jeff Miller, Daniel Carpenter

2024

Abstract

Automated planning tools play a large and expanding role in the function of many parts of our lives. The complex nature of the planning problem and the increasing amount of information the human planner must synthesize indicate that assistive automation must soon become the norm. Despite this, many existing automated planners are incapable of producing plans that reflect the desires and expertise of their operators. They do not have the direct ability to consider the operators’ priorities, nor can they exploit expert operational knowledge that comes from human experience and not data systems. In this paper we present methods to learn operator planning preferences and then nudge our automated logistic planner to produce plans that are better aligned with operator preferences without changing the code of the planner.

Paper Citation

in Harvard Style

Yaman F., Eskridge T., Scott R., Lin L., Miller J. and Carpenter D. (2024). Nudging Automated Planners with Learned User Preferences. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 958-964. DOI: 10.5220/0012423600003636

in Bibtex Style

@conference{icaart24,
author={Fusun Yaman and Thomas Eskridge and Ron Scott and Li Lin and Jeff Miller and Daniel Carpenter},
title={Nudging Automated Planners with Learned User Preferences},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={958-964},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012423600003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Nudging Automated Planners with Learned User Preferences
SN - 978-989-758-680-4
AU - Yaman F.
AU - Eskridge T.
AU - Scott R.
AU - Lin L.
AU - Miller J.
AU - Carpenter D.
PY - 2024
SP - 958
EP - 964
DO - 10.5220/0012423600003636
PB - SciTePress