In this paper, a human-autonomy interaction approach is presented that enables autonomy to proactively dialogue with human teammates to maintain common understanding of the underlying processes. A class of human-autonomy systems where the role of the autonomy is to assist a human teammate in decision making tasks is considered. The autonomy maintains its knowledge of the processes and the environment in a Bayesian engine, and uses a Bayesian inference framework to provide decision support. Any discrepancy in the knowledge of the process between the autonomy and the human teammate may lead to inefficient decision support. The presented curious partner interaction framework uses a dialogue-based approach to resolve differences between the human and the autonomy. The dialog acts as a feedback mechanism to revise the Bayesian engine representation of the autonomy’s knowledge to establish common ground. An application to military operations is considered where a digital assistant uses the curious partner framework to provide decision support to a commander.
S. S. Mehta, E. A. Doucette, and J. W. Curtis, "Curious partner: an autonomous system that proactively dialogues with human teammates," Proc. SPIE 10194, Micro- and Nanotechnology Sensors, Systems, and Applications IX, 101941W (Presented at SPIE Defense + Security: April 12, 2017; Published: 18 May 2017); https://doi.org/10.1117/12.2263205.
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