Dynamic Task Allocation for Human-Robot Teams
conference paper
Artificial agents, such as robots, are increasingly deployed for teamwork in dynamic, high-demand environments. This paper presents a framework, which applies context information to establish task (re)allocations that improve human-robot team’s performance. Based on the framework, a model for adaptive automation was designed that takes the cognitive task load (CTL) of a human team member and the coordination costs of switching to a new task allocation into account. Based on these two context factors, it tries to optimize the level of autonomy of a robot for each task. The model was instantiated for a single human agent cooperating with a single robot in the urban search and rescue domain. A first experiment provided encouraging results: the cognitive task load of participants mostly reacted to the model as intended. Recommendations for improving the model are provided, such as adding more context information.
Topics
RoboticsTask allocationTeamworkAgent CooperationHuman-robot InteractionArtificial intelligenceHuman resource managementRobotsSemanticsAdaptive automationAgent cooperationArtificial agentsContext informationCoordination costsDynamic task allocationLevel of autonomiesUrban search and rescueHuman robot interaction
TNO Identifier
521647
Source title
Proceedings of the 7th International Conference on Agents and Artificial Intelligence, Lisbon, Portugal. January 10-12, 2015 ICAART2015
Pages
117-124
Files
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