Collaborative Research: SOTERIA: Satisfaction and Risk-aware Dynamic Resource Orchestration in Public Safety Systems

合作研究：SOTERIA：公共安全系统中的满意度和风险意识动态资源编排

• 批准号: 2319995
• 负责人: Sajal Das
• 金额: $ 29.98万
• 依托单位: Missouri University of Science and Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319995&HistoricalAwards=false
• 关键词: Collaborative; Research; SOTERIA; Satisfaction; Risk

Public Safety Systems (PSS) deal with victims, first responders, and emergency control centers (ECCs) that coordinate the rescue missions during and aftermath of a disaster. Currently, there is limited understanding of how humans make decisions to utilize personal and shared resources in the wake of disasters; moreover, the existing models are mostly qualitative. Limited information availability in disaster scenarios and resource uncertainty in PSSs pose two major challenges: (1) how to achieve the victims’ satisfaction accounting for their risk-aware (autonomous) decision-making characteristics about dynamic resource orchestration? and (2) how to design a resilient disaster response system incentivizing the victims’ participation in crowdsourcing? The innovative SOTERIA project addresses these challenges by proposing a novel behavioral decision-making model that captures humans’ decision-making characteristics under risk, stemming from uncertain availability of resources. It introduces a new field in game theory, called Satisfaction Games that aims to satisfy the victims’ minimum service requirements rather than maximizing their overall satisfaction, addressing the resource management problems in PSSs. A novel bio-inspired Disaster Response Network (DRN) is also proposed that mimics the inherent robustness of biological networks of living organisms, to support the victims’ participation in reliable crowdsourcing, while the truthfulness and quality of the collected information are evaluated based on the newly invented Bayesian Prospect Theory. The proposed methods and the system will be evaluated using real-world field data. The SOTERIA project will create new distributed control, optimization, and scalable resource management techniques in PSSs. The novelty lies in the integrated approach to efficiently managing resources considering human behavior and Tragedy of the Commons phenomena about shared resources, thus enhancing the victim’s satisfaction instead of utility maximization. To deal with incomplete information about resource availability, the proposed reinforcement learning techniques will enhance the victim’s decision-making capability in real time. Research outcomes of this project have tremendous potential for supporting ECC activities and saving peoples’ lives and infrastructures during and after disasters, by designing robust situation-aware disaster response networks and advancing state of the art research in Prospect Theory, Tragedy of the Commons, and Satisfaction Games. The project will train undergraduate and graduate students and the research findings will be disseminated via a project website and high-quality publications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

公共安全系统（PSS）与受害者、急救人员和紧急控制中心（ECC）打交道，这些中心负责协调灾难期间和灾难后的救援任务。目前，人们对人类如何在灾难中利用个人和共享资源做出决策的了解还很有限。此外，现有模型大多是定性的，灾害场景中的信息可用性有限，而公共服务系统中的资源不确定性带来了两个主要挑战：（1）如何实现受害者的满意度，以考虑他们的风险意识（自主）决策。 - 制定动态特征资源编排？以及（2）如何设计一个有弹性的灾难响应系统来激励受害者参与众包？创新的 SOTERIA 项目通过提出一种新的行为决策模型来应对这些挑战，该模型可以捕捉人类在风险下的决策特征，它引入了博弈论中的一个新领域，称为满意度游戏，旨在满足受害者的最低服务要求，而不是最大化他们的整体满意度，解决 PSS 中的资源管理问题。还提出了响应网络（DRN），模仿生物评估网络的固有稳健性，以支持受害者参与可靠的众包，而收集信息的真实性和质量基于新发明的贝叶斯前景理论。所提出的方法和系统将使用真实世界的现场数据进行评估，该项目将在 PSS 中创建新的分布式控制、优化和可扩展的资源管理技术，其新颖之处在于考虑人类行为和资源的综合方法。悲剧为了处理有关资源可用性的不完整信息，所提出的强化学习技术将增强受害者的实时决策能力。通过设计强大的态势感知灾难响应网络并推进前景理论、公地悲剧和满意度游戏方面的最先进研究，该项目将在支持 ECC 活动以及在灾害期间和灾后拯救人们的生命和基础设施方面发挥巨大潜力。培训本科生和研究生，研究成果将通过项目网站和高质量出版物传播。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。