Feng, LuWiltsche, ClemensTopcu, UfukHumphrey, Laura2023-05-222023-05-222015-04-012015-09-28https://repository.upenn.edu/handle/20.500.14332/6864We 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 consider a scenario involving road network surveillance by an unmanned aerial vehicle (UAV) that is controlled remotely by a human operator but also has a certain degree of autonomy. Depending on the type (i.e., probabilistic and/or nondeterministic) of knowledge about the uncertainties and imperfections in the operatorautonomy interactions, we use abstractions based on Markov decision processes and augment these models to stochastic two-player games. Our approach enables the synthesis of operator-dependent optimal mission plans for the UAV, highlighting the effects of operator characteristics (e.g., workload, proficiency, and fatigue) on UAV mission performance; it can also provide informative feedback (e.g., Pareto curves showing the trade-offs between multiple mission objectives), potentially assisting the operator in decision-making.© Owner/Authors | ACM 2015. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems, http://dx.doi.org/10.1145/2735960.2735973.CPS Formal MethodsCPS Embedded Controlprogram synthesisroboticsoperator interfacesuser/machine systemsautomatic programminghuman factorsverificationComputer EngineeringComputer SciencesRoboticsPresentation