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Authors: Michaela Urbanovská and Antonín Komenda

Affiliation: Department of Computer Science (DCS), Faculty of Electrical Engineering (FEE), Czech Technical University in Prague (CTU), Karlovo namesti 293/13, Prague, 120 00, Czech Republic

Keyword(s): Classical Planning, Domain-independent Planning, Neural Networks, Problem Representation.

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 o n 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. (More)

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Paper citation in several formats:
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; ISSN 2184-433X, SciTePress, pages 871-880. DOI: 10.5220/0010918500003116

@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},
issn={2184-433X},
}

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
IS - 2184-433X
AU - Urbanovská, M.
AU - Komenda, A.
PY - 2022
SP - 871
EP - 880
DO - 10.5220/0010918500003116
PB - SciTePress

- Science and Technology Publications, Lda.
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