Synthesis of Human-in-the-Loop Control Protocols for Autonomous Systems


We propose an approach to synthesize control protocols for autonomous systems that account for uncertainties and imperfections in interactions with human operators. As an illustrative example, we think about a situation involving road network surveillance by an unmanned aerial vehicle (UAV) that is controlled remotely by a personality's operator but conjointly features a sure degree of autonomy. Depending on the type (i.e., probabilistic and/or nondeterministic) of knowledge regarding the uncertainties and imperfections in the human–automation interactions, we tend to use abstractions based mostly on Markov call processes and augment these models to stochastic two-player games. Our approach allows the synthesis of operator-dependent optimal mission plans for the UAV, highlighting the results of operator characteristics (e.g., workload, proficiency, and fatigue) on UAV mission performance. It will additionally offer informative feedback (e.g., Pareto curves showing the trade-offs between multiple mission objectives), probably helping the operator in decision-making. We demonstrate the applicability of our approach via an in depth UAV mission designing case study.

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