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Title: Extending MAPE-K to support human-machine teaming
The MAPE-K feedback loop has been established as the primary reference model for self-adaptive and autonomous systems in domains such as autonomous driving, robotics, and Cyber-Physical Systems. At the same time, the Human Machine Teaming (HMT) paradigm is designed to promote partnerships between humans and autonomous machines. It goes far beyond the degree of collaboration expected in human-on-the-loop and human-in-the-loop systems and emphasizes interactions, partnership, and teamwork between humans and machines. However, while MAPE-K enables fully autonomous behavior, it does not explicitly address the interactions between humans and machines as intended by HMT. In this paper, we present the MAPE-K-HMT framework which augments the traditional MAPE-K loop with support for HMT. We identify critical human-machine teaming factors and describe the infrastructure needed across the various phases of the MAPE-K loop in order to effectively support HMT. This includes runtime models that are constructed and populated dynamically across monitoring, analysis, planning, and execution phases to support human-machine partnerships. We illustrate MAPE-KHMT using examples from an autonomous multi-UAV emergency response system, and present guidelines for integrating HMT into MAPE-K.  more » « less
Award ID(s):
1931962
PAR ID:
10359158
Author(s) / Creator(s):
; ; ; ;
Editor(s):
Schmerl, Bradley R.; Maggio, Martina; Camara, Javier
Date Published:
Journal Name:
Proceedings of the International Symposium on Software Engineering for Adaptive and Self-Managing Systems
Volume:
2022
Page Range / eLocation ID:
120 to 131
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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