<p dir="ltr">Cyber-physical-human systems (CPHSs) integrate human cognitive capabilities into the decision and control processes of complex dynamical systems. While artificial intelligence (AI) has shown promise in controlling such systems, it often encounters challenges such as conflict with human behavior and brittleness. Moreover, even successful AI implementations may lead to negative impacts on humans, such as the degradation of manual skills and diminished situation awareness, thereby weakening humans' ability to effectively monitor and intervene in off-nominal conditions as the final decision-makers of the systems. To address these unique challenges within CPHSs, this dissertation proposes three key approaches. First, human behavior modeling approaches are proposed to enhance understanding and prediction of human behavior from the perspective of AI. Accurate modeling enables better calibration of AI's expectations regarding human teammates' intentions and skill-levels. Second, a novel shared control approach is developed to expedite human training for complex dynamic control tasks. An assistant agent supports human novices in emulating human experts by leveraging human behavior models to gauge the human's skill-levels and provide tailored assistance to help improve one's skill. Lastly, human-autonomy teaming (HAT) design is addressed from a resource allocation perspective. A systematic computational simulation approach is proposed to optimize function and attention allocation to manage trade-offs in performance, situation awareness, workload, and other considerations. The proposed frameworks are demonstrated via examples in drone applications. Numerical and experimental results, utilizing simulation platforms and human subjects, validate the efficacy of the proposed approaches. This dissertation presents significant progress in the design and implementation of CPHSs in that it offers insights and methodologies to enhance collaborative interactions between humans and autonomous systems in complex environments.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/25676601 |
| Date | 26 April 2024 |
| Creators | Sooyung Byeon (18431625) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Modeling_Training_and_Teaming_Approaches_for_Cyber-Physical-Human_Systems/25676601 |
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