Learning Heuristics for Topographic Path Planning in Agent-Based Simulations

Henrique Krever, Thiago Leão, Juliano Pasa, Edison P. de Freitas, Raul Nunes, Luis A. L. Silva

2023

Abstract

Path planning algorithms with Deep Neural Networks (DNN) are fundamental to Agent-Based Modeling and Simulation (ABMS). Pathfinding algorithms use various heuristic functions while searching for a route with a low cost according to different criteria. When such algorithms are applied to compute agent routes in simulated terrain maps represented by large numbers of nodes and where topographic movement constraints are present, the problem is that traditional heuristic functions lose quality since they do not capture important characteristics for target simulation problems. To approach this issue, this work investigates the training of DNNs with large numbers of (i) topographic path costs and (ii) correction factors for standard Euclidean distance heuristic estimations. The aim is to use these DNNs as heuristic functions to guide the execution of different A∗ -based topographic path planning algorithms in agent-based simulations. The work approaches the heuristic learning and computation of agent routes in topographic terrain maps of different natures. To assess the performance of the proposed techniques, experimental results with path planning algorithms and alternative topographic maps are analyzed according to statistical models.

Paper Citation

in Harvard Style

Krever H., Leão T., Pasa J., P. de Freitas E., Nunes R. and A. L. Silva L. (2023). Learning Heuristics for Topographic Path Planning in Agent-Based Simulations. In Proceedings of the 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH; ISBN 978-989-758-668-2, SciTePress, pages 115-125. DOI: 10.5220/0012129900003546

in Bibtex Style

@conference{simultech23,
author={Henrique Krever and Thiago Leão and Juliano Pasa and Edison P. de Freitas and Raul Nunes and Luis A. L. Silva},
title={Learning Heuristics for Topographic Path Planning in Agent-Based Simulations},
booktitle={Proceedings of the 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH},
year={2023},
pages={115-125},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012129900003546},
isbn={978-989-758-668-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH
TI - Learning Heuristics for Topographic Path Planning in Agent-Based Simulations
SN - 978-989-758-668-2
AU - Krever H.
AU - Leão T.
AU - Pasa J.
AU - P. de Freitas E.
AU - Nunes R.
AU - A. L. Silva L.
PY - 2023
SP - 115
EP - 125
DO - 10.5220/0012129900003546
PB - SciTePress