Christopher J. Shannon

Papers

Total Citations

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About

Christopher J. Shannon is a researcher at the forefront of human-autonomy teaming, focusing on how humans and robots can collaborate effectively in complex, dynamic missions. His key research areas include adaptive mission planning, multi-agent task allocation, and human performance modeling. Shannon’s major contribution lies in developing fast, adaptive algorithms that integrate realistic human performance models into the planning process, addressing the challenge that traditional scheduling approaches fail when humans and robots must coordinate closely under uncertainty. His most-cited work, "Adaptive mission planning for coupled human-robot teams" (2016, 10 citations), introduces novel techniques to account for the stochastic nature of human behavior, enabling more resilient and efficient teaming. This work is complemented by a pilot study on flexible human performance models (8 citations) and his MIT master’s thesis (2 citations), which laid the foundation for his approach. Shannon’s research is particularly notable for bridging computational planning with cognitive science, offering practical pathways for real-world applications in search-and-rescue, disaster response, and military operations. His contributions are shaping the next generation of human-robot collaboration, making autonomous systems more responsive and trustworthy partners.