Characterization of Indicators for Adaptive Human-Swarm Teaming

Abstract

Swarm systems consist of large numbers of agents that collaborate autonomously. With an appropriate level of human control, swarm systems could be applied in a variety of contexts ranging from urban search and rescue situations to cyber defence. However, the successful deployment of the swarm in such applications is conditioned by the effective coupling between human and swarm. While adaptive autonomy promises to provide enhanced performance in human-machine interaction, distinct factors must be considered for its implementation within human-swarm interaction. This paper reviews the multidisciplinary literature on different aspects contributing to the facilitation of adaptive autonomy in human-swarm interaction. Specifically, five aspects that are necessary for an adaptive agent to operate properly are considered and discussed, including mission objectives, interaction, mission complexity, automation levels, and human states. We distill the corresponding indicators in each of the five aspects, and propose a framework, named MICAH (i.e., Mission-Interaction-Complexity-Automation-Human), which maps the primitive state indicators needed for adaptive human-swarm teaming.

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“…Enhancing the operator's trust involves providing them with accurate and timely feedback about the swarm's state, thereby improving the decision-making process and task performance [11]. The challenge lies in designing interfaces that provide simplified, but informative feedback to operators, enabling them to make informed decisions without being overwhelmed by the swarm's complexities [12]. A possible holistic vision is a "joint human-swarm loop" in which the human becomes a part of the swarm and the robot an extension of the human [13].…”

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“…Effective human–machine interaction requires that the machine to adapt its behavior according to the user’s cognitive and emotional states, behaviors, performance, and other personal information including identity [ 13 ]. Cognitive biometrics support dynamic recognition of a user’s identity and cognitive and emotional states [ 14 ] and, therefore, are important tools for human–machine interaction in social robots, human–machine systems, and human–swarm teaming systems [ 15 ]; Adaptive control. In closed-loop human–machine adaptive systems, the adaptive control aims to automatically update system parameters by associating the user and system states, so that the user and system can work together effectively and harmoniously.…”

“…Effective human–machine interaction requires that the machine to adapt its behavior according to the user’s cognitive and emotional states, behaviors, performance, and other personal information including identity [ 13 ]. Cognitive biometrics support dynamic recognition of a user’s identity and cognitive and emotional states [ 14 ] and, therefore, are important tools for human–machine interaction in social robots, human–machine systems, and human–swarm teaming systems [ 15 ];…”