CAMP-IRL Agents: Extracting Multiple Behavior Profiles for Pedestrian Simulation

Nahum Alvarez, Itsuki Noda

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.

Download


Paper Citation


in Harvard Style

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, pages 216-223. DOI: 10.5220/0010209602160223


in Bibtex Style

@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},
}


in EndNote Style

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
AU - Alvarez N.
AU - Noda I.
PY - 2021
SP - 216
EP - 223
DO - 10.5220/0010209602160223