计算机科学
人在回路中
平均绝对百分比误差
人工智能
机器人学
机器学习
分布式计算
机器人
人工神经网络
作者
Jane Cleland‐Huang,T P Chambers,Sebastián Zudaire,Muhammed Tawfiq Chowdhury,Ankit Agrawal,Michael Vierhauser
出处
期刊:ACM Transactions on Autonomous and Adaptive Systems
[Association for Computing Machinery]
日期:2023-09-04
摘要
The Human Machine Teaming (HMT) paradigm focuses on supporting partnerships between humans and autonomous machines. HMT describes requirements for transparency, augmented cognition, and coordination that enable far richer partnerships than those found in typical human-on-the-loop and human-in-the-loop systems. Autonomous, self-adaptive systems in domains such as autonomous driving, robotics, and Cyber-Physical Systems, are often implemented using the MAPE-K feedback loop as the primary reference model. However, while MAPE-K enables fully autonomous behavior, it does not explicitly address the interactions that occur between humans and autonomous machines as intended by HMT. In this paper, we, therefore, present the MAPE-K HMT framework which utilizes runtime models to augment the monitoring, analysis, planning, and execution phases of the MAPE-K loop in order to support HMT despite the different operational cadences of humans and machines. We draw on examples from our own emergency response system of interactive, autonomous, small unmanned aerial systems to illustrate the application of MAPE-K HMT in both a simulated and physical environment, and discuss how the various HMT models are connected and can be integrated into a MAPE-K solution.
科研通智能强力驱动
Strongly Powered by AbleSci AI