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Towards a Cognitively-Based Analytic Model of Human Control of Swarms

Robotic swarms are nonlinear dynamical systems that benefit from the presence of human operators in realistic missions with changing goals and constraints. There has been recent interest in safe operations of robotic swarms under human control. Verification and validation techniques for these  human-machine systems could be deployed to provide formal guarantees of safe performance. A current limitation that hinders practical significant applications of verification to human-machine systems is the lack of analytic models of the human operator that include realistic cognitive constraints. Our research aims to develop  high fidelity analytic human models via abstraction of cognitive models based on ACT-R. In this paper, we report on results from the first step in this process. We designed a 2-choice control task of a robotic swarm, obtained data from human operators and compared the fit of these data to two analytic models and an ACT-R based cognitive model. We present the experimental results and discuss our future plans on how the analytic model will be derived from the cognitive model so that the whole human-swarm system could be amenable to verification techniques.