Learning Heuristic Estimates for Planning in Grid Domains by Cellular Simultaneous Recurrent Networks
Michaela Urbanovská, Antonín Komenda
2022
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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in Harvard Style
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, pages 203-213. DOI: 10.5220/0010813900003116
in Bibtex Style
@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},
}
in EndNote Style
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
AU - Urbanovská M.
AU - Komenda A.
PY - 2022
SP - 203
EP - 213
DO - 10.5220/0010813900003116