MDPs for Adjustable Autonomy in Real-World Multi-Agent Environments

David Pynadath, Paul Scerri, and Milind Tambe

Research on adjustable autonomy (AA) is critical if we are deploy multiagent systems in support of important human activities. Through AA, an agent can dynamically vary its level of autonomy -- harnessing human abilities when needed, but also limiting such interaction. While most previous AA work has focused on individual agent-human interactions, this paper focuses on agent teams embedded in human organizations in the context of real-world applications. The need for agent teamwork and coordination in such environments introduces novel AA challenges. In particular, transferring control to human users becomes more difficult, as a lack of human response can cause agent team miscoordination, yet not transferring control causes agents to take enormous risks. Furthermore, despite appropriate individual agent decisions, the agent teams may reach decisions that are completely unacceptable to the human team. We address these challenges by pursuing a two-part decisiontheoretic approach. First, to avoid team miscoordination due to transfer of control decisions, an agent: (i) considers the cost of potential miscoordination with teammates; (ii) does not rigidly commit to a transfer of control decision; (iii) if forced into a risky autonomous action to avoid miscoordination, considers changes in the team’s coordination that mitigate the risk. Second, to ensure effective team decisions, not only individual agents, but also subteams and teams can dynamically adjust their own autonomy. We implement these ideas using Markov Decision Processes, providing a decision-theoretic basis for reasoning about costs and uncertainty of individual and team actions. This approach is central to our deployed multi-agent system, called Electric Elves, that assists our research group in rescheduling meetings, choosing presenters, tracking people’s locations and ordering meals.


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