AI-empowered data-driven agent-based modeling and simulation: challenges, methodologies, and future perspectives
AI-empowered data-driven agent-based modeling and simulation: challenges, methodologies, and future perspectives
Agent-based modeling and simulation (ABMS) has become one of the most popular simulation methods for scientific research and real-world applications. This tutorial paper explores recent development in the use of artificial intelligence including large-language models and machine learning, and digital twin in ABMS research. Given the different perspectives on ABMS, this paper will start with ABMS basic concepts and their implementation using an online platform called AgentBlock.net.
Onggo, Bhakti S.
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He, Zhou
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Lu, Peng
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Bai, Quan
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Hu, Yuxuan
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Onggo, Bhakti S.
8e9a2ea5-140a-44c0-9c17-e9cf93662f80
He, Zhou
d716f96f-44cb-48da-be6b-a9ecf605237d
Lu, Peng
486850ee-b63f-4ac9-8747-5f3383339a68
Bai, Quan
07ffe06d-8db1-48fd-909a-5c23a68dcc0f
Hu, Yuxuan
50bb043d-adf8-4726-901a-74878700c2eb
Onggo, Bhakti S., He, Zhou, Lu, Peng, Bai, Quan and Hu, Yuxuan
(2025)
AI-empowered data-driven agent-based modeling and simulation: challenges, methodologies, and future perspectives.
In Proceedings of the 2025 Winter Simulation Conference.
IEEE.
15 pp
.
(In Press)
Record type:
Conference or Workshop Item
(Paper)
Abstract
Agent-based modeling and simulation (ABMS) has become one of the most popular simulation methods for scientific research and real-world applications. This tutorial paper explores recent development in the use of artificial intelligence including large-language models and machine learning, and digital twin in ABMS research. Given the different perspectives on ABMS, this paper will start with ABMS basic concepts and their implementation using an online platform called AgentBlock.net.
Text
inv119s2-file1
- Accepted Manuscript
More information
Accepted/In Press date: 3 June 2025
Venue - Dates:
2025 Winter Simulation Conference, , Seattle, United States, 2025-12-07 - 2025-12-10
Identifiers
Local EPrints ID: 504058
URI: http://eprints.soton.ac.uk/id/eprint/504058
PURE UUID: 4839cfeb-def5-4f04-83f7-41d2c927a7c5
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Date deposited: 22 Aug 2025 16:32
Last modified: 23 Aug 2025 02:15
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Contributors
Author:
Zhou He
Author:
Peng Lu
Author:
Quan Bai
Author:
Yuxuan Hu
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