Date of Award
8-2025
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Mechanical Engineering
Committee Chair/Advisor
Dr. Sophie "Yue" Wang
Committee Member
Dr. Oliver Myers
Committee Member
Dr. Melissa Smith
Abstract
At first glance, choosing between an apple and an orange appears to be a straightforward matter of personal taste; however, this seemingly simple preference opens a window into the multifaceted world of decision-making, revealing the complex interplay of cognitive processes, psychological, and behavioral-economic principles that guide our choices \cite{bandyopadhyayRoleAffectDecision2013}. By unpacking these nuanced perspectives, we uncover insights that can drive more effective human-robot interaction and collaboration.
Modeling human cognition requires understanding the evolution of choice utility and the influence of emotions. Decision Field Theory (DFT) stands out by capturing the fluctuating nature in human preferences over time, explaining why choices are inconsistent under stress and/or uncertainty. This study leverages sequential-sampling to analyze human response and interaction, while machine learning technique is used to derive and optimize DFT parameters. This enables robots to adapt their decision-making process based on human-cognitive dynamics. Conversely, the approach also allows robots to better gauge human-risk attitude, fostering a more effective collaboration.
This thesis explores evolving decision-making theories shifting from traditional Expected Utility and Non-Expected Utility models toward frameworks that incorporate the influence of human emotions on judgment with respect to time. We review insights into their respective strengths and limitations. Machine learning framework grounded in cognitive science are designed and implemented to model user preferences, and situational judgment under uncertainty. The experiments validate the integration of trust, risk attitudes, and adaptive decision-making in human-robot interaction (HRI), demonstrating human-centric decision-making processes. Applications such as human-robot pairing in manufacturing warehouses and bounding overwatch scenarios illustrate how incorporating human-centric decision principles may potentially enhance situational awareness and operational flexibility in robots, fostering more intuitive collaboration.
Recommended Citation
Mbagna Nanko, Ryan, "Decision Field Theory for Human-Multi-robot Collaboration: Human-centric decision-making for multi-robot systems" (2025). All Theses. 4602.
https://open.clemson.edu/all_theses/4602
Author ORCID Identifier
0009-0007-4479-3697
Included in
Acoustics, Dynamics, and Controls Commons, Behavioral Economics Commons, Cognitive Psychology Commons, Cognitive Science Commons, Human Factors Psychology Commons, Navigation, Guidance, Control, and Dynamics Commons