Through adjustable autonomy (AA), an agent can dynamically vary the degree to which it acts autonomously, allowing it to exploit human abilities to improve its performance, but without becoming overly dependent and intrusive. AA research is critical for successful deployment of agents to support important human activities. While most previous work has focused on individual agent-human interactions, this paper focuses on teams of agents operating in real-world human organizations, as well as the novel AA coordination challenge that arises when one agent’s inaction while waiting for a human response can lead to potential miscoordination. Our multi-agent AA framework, based on Markov decision processes, provides an adaptive model of users that reasons about the uncertainty, costs, and constraints of decisions. Our approach to AA has proven essential to the success of our deployed Electric Elves system that assists our research group in rescheduling meetings, choosing presenters, tracking people’s locations, and ordering meals.
KeywordsMarkov Decision Process Intelligent Agent Reward Function Meeting Location Agent Team
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