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Leveraging explainable AI for enhanced decision making in humanitarian logistics: An adversarial coevolution (ACTION) framework

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journal contribution
posted on 2023-10-17, 00:33 authored by Su NguyenSu Nguyen, Greg O’Keefe, Sobhan AsianSobhan Asian, Kerry Trentelman, Damminda AlahakoonDamminda Alahakoon

Abstract: This study examines the potential of AI-enabled wargames to enhance strategic decisionmaking in humanitarian assistance and disaster relief (HADR). We introduce an Adversarial CoevoluTION (ACTION) framework, which showcases AI’s capacity to evolve adaptable policies capable of responding to dynamic changes and adversarial actions in HADR wargame scenarios. The framework presented employs a grammar-based genetic programming algorithm to evolve intelligent and interpretable player policies. We apply the ACTION framework to a HADR wargame case study, commonly used by the Australian Defence Science and Technology Group for research purposes. The case study centres on a hypothetical disaster relief scenario in the fictional Joadia Islands, struck by a tsunami, necessitating the evacuation of dispersed civilians. Experimental results illustrate that the ACTION framework can evolve policies that adapt to environmental uncertainties and respond effectively to adversarial actions. This study offers evidence of the potential and practical application of AI-enabled technology in real-life humanitarian situations. Our findings suggest practical guidelines for humanitarian practitioners to enhance the efficiency and effectiveness of logistics planning for humanitarian aid, ultimately leading to improved outcomes in HADR scenarios.

History

Publication Date

2023-10-15

Journal

International Journal of Disaster Risk Reduction

Volume

97

Article Number

104004

Pagination

19p.

Publisher

Elsevier

ISSN

2212-4209

Rights Statement

© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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