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Carnegie Mellon Robotics Institute

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Abstract

Task allocation impacts the performance efficiency of agent teams in significant ways. Due to their efficient and proven performance, Market-based task allocation approaches have grown in popularity for many such multi-agent domains. In addition, market-based approaches are very well suited to dynamic domains such as emergency response, in which the set of the tasks or the environment changes in real time. However, market-based approaches are not guaranteed to produce optimal solutions and researchers have investigated many techniques for improving their performance in different scenarios. Since many application domains have a significant static component coupled with dynamic elements, we explore the option of enhancing team performance in these domains by seeding market-based task allocation with optimal schedules pre-computed for the static tasks. We compare the performance of the TraderBots market-based algorithm with and without the seeded optimal schedules in simulation and on a team of robots. Our results demonstrate that seeding market-based allocation with optimal schedules can improve team performance, particularly when the proportion of static tasks is high.

Notes

Associated Center(s) / Consortia: Field Robotics Center

Associated Lab(s) / Group(s): rCommerce

Associated Project(s): Coordination for Disaster Response

Text Reference

G. Ayorkor Korsah, Balajee Kannan, Imran Aslam Fanaswala, and M Bernardine Dias, "Enhancing Market-Based Task Allocation with Optimal Initial Schedules," Intelligent Autonomous Systems 11 - IAS-11, August, 2010, pp. 249 - 258.

BibTeX Reference

@inproceedings{Korsah_2010_6777,
   author = "G. Ayorkor Korsah and Balajee Kannan and Imran Aslam Fanaswala and M Bernardine Dias",
   title = "Enhancing Market-Based Task Allocation with Optimal Initial Schedules",
   booktitle = "Intelligent Autonomous Systems 11 - IAS-11",
   pages = "249 - 258",
   month = "August",
   year = "2010",
}

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