Curious partner: An approach to realize common ground in human-autonomy collaboration

摘要

A dialog-based human-autonomy interaction ap- proach, called curious partner, is presented for a class of systems where the role of autonomy is to assist humans in decision making tasks. Even if the human and the autonomy share the environment and receive identical information, they may have inconsistencies in the representation of the environment due to difference in their perception and expert knowledge. The curious partner interaction framework is presented to resolve model-level differences between the human and the autonomy to establish common ground. The knowledge base of the autonomy is modeled using a Bayesian engine. The autonomy's dialog with the human acts as a feedback mechanism to resolve any differences either by suggesting maximally probable actions to human based on the state of its Bayesian model or by updating its model to achieve analogous world representation.