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Improving Human-Machine Collaboration Through Transparency-Based Feedback – Part I: Human Trust And Workload Model, Kumar Akash, Katelyn Polson, Neera Jain
Improving Human-Machine Collaboration Through Transparency-Based Feedback – Part I: Human Trust And Workload Model, Kumar Akash, Katelyn Polson, Neera Jain
School of Mechanical Engineering Faculty Publications
In this paper, we establish a partially observable Markov decision process(POMDP) model framework that captures dynamic changes in human trust and workload for contexts that involve interactions between humans and intelligent decision-aid systems. We use a reconnaissance mission study to elicit a dynamic change in human trust and workload with respect to the system’s reliability and user interface transparency as well as the presence or absence of danger. We use human subject data to estimate transition and observation probabilities of the POMDP model and analyze the trust-workload behavior of humans. Our results indicate that higher transparency is more likely to …
Improving Human-Machine Collaboration Through Transparency-Based Feedback – Part Ii: Control Design And Synthesis, Kumar Akash, Tahira Reid, Neera Jain
Improving Human-Machine Collaboration Through Transparency-Based Feedback – Part Ii: Control Design And Synthesis, Kumar Akash, Tahira Reid, Neera Jain
School of Mechanical Engineering Faculty Publications
To attain improved human-machine collaboration, it is necessary for autonomous systems to infer human trust and workload and respond accordingly. In turn, autonomous systems require models that capture both human trust and workload dynamics. In a companion paper, we developed a trust-workload partially observable Markov decision process (POMDP) model framework that captured changes in human trust and workload for contexts that involve interaction between a human and an intelligent decision-aid system. In this paper, we defne intuitive reward functions and show that these can be readily transformed for integration with the proposed POMDP model. We synthesize a near-optimal control policy …