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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, Simultaneous Recurrent Neural Networks, Heuristic Learning, Deep Learning.

Abstract: Automated planning provides a powerful general problem solving tool, however, its need for a model creates a bottleneck that can be an obstacle for using it in real-world settings. In this work we propose to use neural networks, namely Cellular Simultaneous Recurrent Networks (CSRN), to process a planning problem and provide a heuristic value estimate that can more efficiently steer the automated planning algorithms to a solution. Using this particular architecture provides us with a scale-free solution that can be used on any problem domain represented by a planar grid. We train the CSRN architecture on two benchmark domains, provide analysis of its generalizing and scaling abilities. We also integrate the trained network into a planner and compare its performance against commonly used heuristic functions.

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Paper citation in several formats:
Urbanovská, M. and Komenda, A. (2022). Learning Heuristic Estimates for Planning in Grid Domains by Cellular Simultaneous Recurrent Networks. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 203-213. DOI: 10.5220/0010813900003116

@conference{icaart22,
author={Michaela Urbanovská. and Antonín Komenda.},
title={Learning Heuristic Estimates for Planning in Grid Domains by Cellular Simultaneous Recurrent Networks},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2022},
pages={203-213},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010813900003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Learning Heuristic Estimates for Planning in Grid Domains by Cellular Simultaneous Recurrent Networks
SN - 978-989-758-547-0
IS - 2184-433X
AU - Urbanovská, M.
AU - Komenda, A.
PY - 2022
SP - 203
EP - 213
DO - 10.5220/0010813900003116
PB - SciTePress

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