Dynamic Task Allocation for Human-robot Teams

Tinka R. A. Giele, Tina Mioch, Mark A. Neerincx, John-Jules C. Meyer


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.


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Paper Citation

in Harvard Style

R. A. Giele T., Mioch T., A. Neerincx M. and C. Meyer J. (2015). Dynamic Task Allocation for Human-robot Teams . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-073-4, pages 117-124. DOI: 10.5220/0005178001170124

in Bibtex Style

author={Tinka R. A. Giele and Tina Mioch and Mark A. Neerincx and John-Jules C. Meyer},
title={Dynamic Task Allocation for Human-robot Teams},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},

in EndNote Style

JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Dynamic Task Allocation for Human-robot Teams
SN - 978-989-758-073-4
AU - R. A. Giele T.
AU - Mioch T.
AU - A. Neerincx M.
AU - C. Meyer J.
PY - 2015
SP - 117
EP - 124
DO - 10.5220/0005178001170124