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1. Extending MAPE-K to support human-machine teaming

   https://doi.org/10.1145/3524844.3528054  

   Cleland-Huang, Jane; Agrawal, Ankit; Vierhauser, Michael; Murphy, Michael; Prieto, Mike (May 2022, Proceedings of the International Symposium on Software Engineering for Adaptive and Self-Managing Systems)

   Schmerl, Bradley R.; Maggio, Martina; Camara, Javier (Ed.)

   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. 

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2. Explaining Autonomous Decisions in Swarms of Human-on-the-Loop Small Unmanned Aerial Systems

   Ankit Agrawal; Jane Cleland-Huang (October 2021, Proceedings the AAAI Conference on Human Computation and Crowdsourcing)

   Rapid advancements in Artificial Intelligence have shifted the focus from traditional human-directed robots to fully autonomous ones that do not require explicit human control. These are commonly referred to as Human-on-the-Loop (HotL) systems. Transparency of HotL systems necessitates clear explanations of autonomous behavior so that humans are aware of what is happening in the environment and can understand why robots behave in a certain way. However, in complex multi-robot environments, especially those in which the robots are autonomous and mobile, humans may struggle to maintain situational awareness. Presenting humans with rich explanations of autonomous behavior tends to overload them with lots of information and negatively affect their understanding of the situation. Therefore, explaining the autonomous behavior of multiple robots creates a design tension that demands careful investigation. This paper examines the User Interface (UI) design trade-offs associated with providing timely and detailed explanations of autonomous behavior for swarms of small Unmanned Aerial Systems (sUAS) or drones. We analyze the impact of UI design choices on human awareness of the situation. We conducted multiple user studies with both inexperienced and expert sUAS operators to present our design solution and initial guidelines for designing the HotL multi-sUAS interface. 

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3. HIFuzz: Human Interaction Fuzzing for Small Unmanned Aerial Vehicles

   https://doi.org/10.1145/3613904.3642958  

   Chambers, Theodore; Vierhauser, Michael; Agrawal, Ankit; Murphy, Michael; Brauer, Jason Matthew; Purandare, Salil; Cohen, Myra B; Cleland-Huang, Jane (May 2024, ACM)

   Small Unmanned Aerial Systems (sUAS) must meet rigorous safety standards when deployed in high-stress emergency response scenarios; however many reported accidents have involved humans in the loop. In this paper, we, therefore, present the HiFuzz testing framework, which uses fuzz testing to identify system vulnerabilities associated with human interactions. HiFuzz includes three distinct levels that progress from a low-cost, limited-fidelity, large-scale, no-hazard environment, using fully simulated Proxy Human Agents, via an intermediate level, where proxy humans are replaced with real humans, to a high-stakes, high-cost, real-world environment. Through applying HiFuzz to an autonomous multi-sUAS system-under-test, we show that each test level serves a unique purpose in revealing vulnerabilities and making the system more robust with respect to human mistakes. While HiFuzz is designed for testing sUAS systems, we further discuss its potential for use in other Cyber-Physical Systems. 

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4. HIFuzz: Human Interaction Fuzzing for Small Unmanned Aerial Vehicles

   https://doi.org/10.1145/3613904  

   Chambers, Theodore; Vierhauser, Michael; Agrawal, Ankit; Murphy, Michael; Brauer, Jason Matthew; Purandare, Salil; Cohen, Myra B; Cleland-Huang, Jane (May 2024, ACM)

   Small Unmanned Aerial Systems (sUAS) must meet rigorous safety standards when deployed in high-stress emergency response scenarios; however many reported accidents have involved humans in the loop. In this paper, we, therefore, present the HiFuzz testing framework, which uses fuzz testing to identify system vulnerabilities associated with human interactions. HiFuzz includes three distinct levels that progress from a low-cost, limited-fidelity, large-scale, no-hazard environment, using fully simulated Proxy Human Agents, via an intermediate level, where proxy humans are replaced with real humans, to a high-stakes, high-cost, real-world environment. Through applying HiFuzz to an autonomous multi-sUAS system-under-test, we show that each test level serves a unique purpose in revealing vulnerabilities and making the system more robust with respect to human mistakes. While HiFuzz is designed for testing sUAS system, we further show that it is applicable across a broader range of Cyber-Physical Systems. 

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5. Model-Driven Requirements for Humans-on-the-Loop Multi-UAV Missions

   https://doi.org/10.1109/MoDRE51215.2020.00007  

   Agrawal, Ankit; Cleland-Huang, Jane; Steghofer, Jan-Philipp (August 2020, Model-Driven Requirements Engineering)

   null (Ed.)

   The use of semi-autonomous Unmanned Aerial Vehicles (UAVs or drones) to support emergency response scenarios, such as fire surveillance and search-and-rescue, has the potential for huge societal benefits. Onboard sensors and artificial intelligence (AI) allow these UAVs to operate autonomously in the environment. However, human intelligence and domain expertise are crucial in planning and guiding UAVs to accomplish the mission. Therefore, humans and multiple UAVs need to collaborate as a team to conduct a time-critical mission successfully. We propose a meta-model to describe interactions among the human operators and the autonomous swarm of UAVs. The meta-model also provides a language to describe the roles of UAVs and humans and the autonomous decisions. We complement the meta-model with a template of requirements elicitation questions to derive models for specific missions. We also identify common scenarios where humans should collaborate with UAVs to augment the autonomy of the UAVs. We introduce the meta-model and the requirements elicitation process with examples drawn from a search-and-rescue mission in which multiple UAVs collaborate with humans to respond to the emergency. We then apply it to a second scenario in which UAVs support first responders in fighting a structural fire. Our results show that the meta-model and the template of questions support the modeling of the human-on-the-loop human interactions for these complex missions, suggesting that it is a useful tool for modeling the human-on-the-loop interactions for multi-UAVs missions. 

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