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When human-multiagent teams act in real-time uncertain domains, adjustable autonomy (dynamic transferring of decisions between human and agents) raises three key challenges. First, the human and agents may differ significantly in their worldviews, leading to inconsistencies in their decisions. Second, these human-multiagent teams must operate and plan in real-time with deadlines with uncertain duration of human actions. Thirdly, adjustable autonomy in teams is an inherently distributed and complex problem that cannot be solved optimally and completely online. To address these challenges, our paper presents a solution for Resolving Inconsistencies in Adjustable Autonomy in Continuous Time (RIAACT). RIAACT incorporates models of the resolution of inconsistencies, continuous time planning techniques, and hybrid method to address coordination complexity. These contributions have been realized in a disaster response simulation system.

RIAACT: a robust approach to adjustable autonomy for human-multiagent teams