Grid Representation in Neural Networks for Automated Planning

Michaela Urbanovská, Antonín Komenda

2022

Abstract

Automated planning and machine learning create a powerful combination of tools which allows us to apply general problem solving techniques to problems that are not modeled using classical planning techniques. In real-world scenarios and complex domains, creating a standardized representation is often a bottleneck as it has to be modeled by a human. That often limits the usage of planning algorithms to real-world problems. The standardized representation is also not a suitable for neural network processing and often requires further transformation. In this work, we focus on presenting three different grid representations that are well suited to model a variety of classical planning problems which can be then processed by neural networks without further modifications. We also analyze classical planning benchmarks in order to find domains that correspond to our proposed representations. Furthermore, we also show that domains that are not explicitly defined on a grid can be represented on a grid with minor modifications that are domain specific. We discuss advantages and drawbacks of our proposed representations, provide examples for many planning benchmarks and also discuss the importance of data and its structure when training neural networks for planning.

Paper Citation

in Harvard Style

Urbanovská M. and Komenda A. (2022). Grid Representation in Neural Networks for Automated Planning. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 871-880. DOI: 10.5220/0010918500003116

in Bibtex Style

@conference{icaart22,
author={Michaela Urbanovská and Antonín Komenda},
title={Grid Representation in Neural Networks for Automated Planning},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={871-880},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010918500003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Grid Representation in Neural Networks for Automated Planning
SN - 978-989-758-547-0
AU - Urbanovská M.
AU - Komenda A.
PY - 2022
SP - 871
EP - 880
DO - 10.5220/0010918500003116