Kordoni Anastasia, Gavidia-Calderon Carlos, Levine Mark, Bennaceur Amel, Nuseibeh Bashar
Department of Psychology, Lancaster University, Lancaster, United Kingdom.
School of Computing and Communications, The Open University, Milton Keynes, United Kingdom.
Front Psychol. 2023 Sep 7;14:1146056. doi: 10.3389/fpsyg.2023.1146056. eCollection 2023.
Autonomous systems, such as drones, are critical for emergency mitigation, management, and recovery. They provide situational awareness and deliver communication services which effectively guide emergency responders' decision making. This combination of technology and people comprises a socio-technical system. Yet, focusing on the use of drone technology as a solely operational tool, underplays its potential to enhance coordination between the different agents involved in mass emergencies, both human and non-human. This paper proposes a new methodological approach that capitalizes on social identity principles to enable this coordination in an evacuation operation. In the proposed approach, an adaptive drone uses sensor data to infer the group membership of the survivors it encounters during the operation. A corpus of 200 interactions of survivors' talk during real-life emergencies was computationally classified as being indicative of a shared identity or personal/no identity. This classification model, then, informed a game-theoretic model of human-robot interactions. Bayesian Nash Equilibrium analysis determined the predicted behavior for the human agent and the strategy that the drone needs to adopt to help with survivor evacuation. Using linguistic and synthetic data, we show that the identity-adaptive architecture outperformed two non-adaptive architectures in the number of successful evacuations. The identity-adaptive drone can infer which victims are likely to be helped by survivors and where help from emergency teams is needed. This facilitates effective coordination and adaptive performance. This study shows decision-making can be an emergent capacity that arises from the interactions of both human and non-human agents in a socio-technical system.
自主系统，如无人机，对于应急缓解、管理和恢复至关重要。它们提供态势感知并提供通信服务，有效地指导应急响应人员的决策。这种技术与人员的结合构成了一个社会技术系统。然而，仅将无人机技术作为一种操作工具来使用，就低估了其增强参与大规模紧急情况的不同主体（包括人类和非人类）之间协调的潜力。本文提出了一种新的方法论方法，该方法利用社会身份原则在疏散行动中实现这种协调。在所提出的方法中，一架自适应无人机利用传感器数据推断其在行动中遇到的幸存者的群体成员身份。对200条现实生活紧急情况中幸存者谈话互动的语料库进行了计算分类，表明其具有共享身份或个人/无身份特征。然后，这种分类模型为一个人机交互的博弈论模型提供了信息。贝叶斯纳什均衡分析确定了人类主体的预测行为以及无人机为帮助幸存者疏散需要采用的策略。使用语言和合成数据，我们表明身份自适应架构在成功疏散的数量上优于两种非自适应架构。身份自适应无人机可以推断哪些受害者可能会得到幸存者的帮助以及哪里需要应急团队的帮助。这促进了有效的协调和自适应性能。这项研究表明，决策可以是一种在社会技术系统中由人类和非人类主体的相互作用产生的涌现能力。
