Xu, Chengtao, Song, Houbing.
2021.
Mixed Initiative Balance of Human-Swarm Teaming in Surveillance via Reinforcement learning. 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC). :1—10.
Human-machine teaming (HMT) operates in a context defined by the mission. Varying from the complexity and disturbance in the cooperation between humans and machines, a single machine has difficulty handling work with humans in the scales of efficiency and workload. Swarm of machines provides a more feasible solution in such a mission. Human-swarm teaming (HST) extends the concept of HMT in the mission, such as persistent surveillance, search-and-rescue, warfare. Bringing the concept of HST faces several scientific challenges. For example, the strategies of allocation on the high-level decision making. Here, human usually plays the supervisory or decision making role. Performance of such fixed structure of HST in actual mission operation could be affected by the supervisor’s status from many aspects, which could be considered in three general parts: workload, situational awareness, and trust towards the robot swarm teammate and mission performance. Besides, the complexity of a single human operator in accessing multiple machine agents increases the work burdens. An interface between swarm teammates and human operators to simplify the interaction process is desired in the HST.In this paper, instead of purely considering the workload of human teammates, we propose the computational model of human swarm interaction (HSI) in the simulated map surveillance mission. UAV swarm and human supervisor are both assigned in searching a predefined area of interest (AOI). The workload allocation of map monitoring is adjusted based on the status of the human worker and swarm teammate. Workload, situation awareness ability, trust are formulated as independent models, which affect each other. A communication-aware UAV swarm persistent surveillance algorithm is assigned in the swarm autonomy portion. With the different surveillance task loads, the swarm agent’s thrust parameter adjusts the autonomy level to fit the human operator’s needs. Reinforcement learning is applied in seeking the relative balance of workload in both human and swarm sides. Metrics such as mission accomplishment rate, human supervisor performance, mission performance of UAV swarm are evaluated in the end. The simulation results show that the algorithm could learn the human-machine trust interaction to seek the workload balance to reach better mission execution performance. This work inspires us to leverage a more comprehensive HST model in more practical HMT application scenarios.
Lee, Hyo-Cheol, Lee, Seok-Won.
2021.
Towards Provenance-based Trust-aware Model for Socio-Technically Connected Self-Adaptive System. 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC). :761—767.
In a socio-technically connected environment, self-adaptive systems need to cooperate with others to collect information to provide context-dependent functionalities to users. A key component of ensuring safe and secure cooperation is finding trustworthy information and its providers. Trust is an emerging quality attribute that represents the level of belief in the cooperative environments and serves as a promising solution in this regard. In this research, we will focus on analyzing trust characteristics and defining trust-aware models through the trust-aware goal model and the provenance model. The trust-aware goal model is designed to represent the trust-related requirements and their relationships. The provenance model is analyzed as trust evidence to be used for the trust evaluation. The proposed approach contributes to build a comprehensive understanding of trust and design a trust-aware self-adaptive system. In order to show the feasibility of the proposed approach, we will conduct a case study with the crowd navigation system for an unmanned vehicle system.