Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Collaborative control: a robot-centric model for vehicle teleoperation
Collaborative control: a robot-centric model for vehicle teleoperation
Kaa: policy-based explorations of a richer model for adjustable autonomy
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
How search and its subtasks scale in N robots
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
A Decision-Theoretic Approach to Cooperative Control and Adjustable Autonomy
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
User Modeling and User-Adapted Interaction
Validating human-robot interaction schemes in multitasking environments
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Autonomous agents dealing with partial knowledge about the environment are a classical subject of study for the decision making community. Moreover, such agents sometimes have to deal with unpredictable situations, which makes any previously computed behavior useless. In this paper, we address such problems using multi-human/multi-robot interactions, where the agents evolve in a complex environment and ask humans for help when they meet unpredicted situations. We introduce a model called HHP-MDP (Human Help Provider MDP), that aims at handling the difficult situations met by the agents by using the human's help. For this purpose, we show how the agents can detect difficult situations and send different types of requests to the set of humans. The model describes how a controller can handle different requests and assign agent requests to the humans by taking into account their previously learned abilities. This controller is designed to reduce the human's cost of bother. Moreover, we show how to optimize the human's situation awareness and limit inconsistencies between her recommendations and the agent's plans.