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Multiagent Systems: Adversarial AgentsHumans and AI: Planning and Decision Support for Human-Machine TeamsSearch and Optimization: Learning to Search
Alleviating Community Fear in Disasters via Multi-Agent Actor-Critic Reinforcement Learning
🤖 AI Summary
This study addresses the challenge of cascading failures across power, communication, and social behavior during disasters, which amplify community panic and undermine collaborative response efforts—limitations exacerbated by the absence of proactive intervention mechanisms in existing models. To overcome this, the authors propose an extended cyber-physical-social resilience framework incorporating control channels from three institutional actors, formulated as a three-player non-zero-sum differential game. The solution leverages online multi-agent actor-critic reinforcement learning to enable, for the first time, coordinated proactive regulation across interdependent infrastructures and human behaviors. Simulations based on real-world data from Hurricane Harvey demonstrate that the approach reduces community panic by 70% on average and significantly accelerates infrastructure recovery. Notably, without retraining, it achieves a 50% reduction in panic under Hurricane Irma scenarios, highlighting strong cross-disaster generalization capability.
Technology Category
Application Category
📝 Abstract
During disasters, cascading failures across power grids, communication networks, and social behavior amplify community fear and undermine cooperation. Existing cyber-physical-social (CPS) models simulate these coupled dynamics but lack mechanisms for active intervention. We extend the CPS resilience model of Valinejad and Mili (2023) with control channels for three agencies, communication, power, and emergency management, and formulate the resulting system as a three-player non-zero-sum differential game solved via online actor-critic reinforcement learning. Simulations based on Hurricane Harvey data show 70% mean fear reduction with improved infrastructure recovery; cross-validation in the case of Hurricane Irma (without refitting) achieves 50% fear reduction, confirming generalizability.
Problem
Research questions and friction points this paper is trying to address.
community fear
cascading failures
cyber-physical-social systems
disaster resilience
infrastructure recovery
Innovation
Methods, ideas, or system contributions that make the work stand out.
multi-agent actor-critic reinforcement learning
cyber-physical-social resilience
non-zero-sum differential game
active intervention
disaster fear mitigation
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