Adjustable autonomy in real-world multi-agent environments
Proceedings of the fifth international conference on Autonomous agents
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Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
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IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Humans-Robots Sliding Collaboration Control in Complex Environments with Adjustable Autonomy
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
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Cooperative control can help overcome the limitations of autonomous systems (AS) by introducing a supervision unit (SU) (human or another system) into the control loop and creating adjustable autonomy. We present a decision-theoretic approach to accomplish this using Mixed Markov Decision Processes (MI-MDPs). The solution is an optimal plan that tells the AS what actions to perform as well as when to request SU attention or transfer control to the SU. This provides a varying degree of autonomy, particularly suitable for robots exploring a domain with regions that are too complex or risky for autonomous operation, or intelligent vehicles operating in heavy traffic.