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

Abstract

This study examines the potential of artificial intelligence-enabled wargames to enhance strategic decision-making in humanitarian assistance and disaster relief (HADR). We introduce the Adversarial CoevoluTION (ACTION) framework, demonstrating AI’s ability to evolve adaptable policies that respond to dynamic changes and adversarial actions in HADR wargame scenarios. Our framework employs a grammar-based genetic programming algorithm to evolve intelligent and interpretable player policies. We apply the framework to a HADR wargame case study, commonly used by the Australian Defence Science and Technology Group for research purposes. Our case study centers 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 uncertainty and respond effectively to adversarial actions. This study provides evidence of AI-enabled technology’s potential and its practical application in real-life humanitarian situations. Our findings offer practical guidelines for humanitarian practitioners to enhance the efficiency and effectiveness of humanitarian aid logistics planning, ultimately improving outcomes in HADR scenarios.

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