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Authors: Nahum Alvarez 1 and Itsuki Noda 2

Affiliations: 1 Airbus Central Research & Technology, Munich, Germany ; 2 National Institute of Advanced Industrial Science and Technology, Japan

Keyword(s): Pedestrian Simulators, Agent Behavior, Inverse Reinforcement Learning.

Abstract: Crowd simulation has been subject of study due to its applications in the fields of evacuation management, smart town planning and business strategic placing. Simulations of human behavior have many useful applications but are limited in their flexibility. A possible solution to that issue is to use semi-supervised machine learning techniques to extract action patterns for the simulation. In this paper, we present a model for agent-based crowd simulation that generates agents capable of navigating efficiently across a map attending to different goal driven behaviors. We designed an agent model capable of using the different behavior patterns obtained from training data, imitating the behavior of the real pedestrians and we compared it with other models attending to behavioral metrics.

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Paper citation in several formats:
Alvarez, N. and Noda, I. (2021). CAMP-IRL Agents: Extracting Multiple Behavior Profiles for Pedestrian Simulation. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-484-8; ISSN 2184-433X, SciTePress, pages 216-223. DOI: 10.5220/0010209602160223

@conference{icaart21,
author={Nahum Alvarez. and Itsuki Noda.},
title={CAMP-IRL Agents: Extracting Multiple Behavior Profiles for Pedestrian Simulation},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2021},
pages={216-223},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010209602160223},
isbn={978-989-758-484-8},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - CAMP-IRL Agents: Extracting Multiple Behavior Profiles for Pedestrian Simulation
SN - 978-989-758-484-8
IS - 2184-433X
AU - Alvarez, N.
AU - Noda, I.
PY - 2021
SP - 216
EP - 223
DO - 10.5220/0010209602160223
PB - SciTePress

- Science and Technology Publications, Lda.
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