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Abstract

This paper presents a learning-enhanced market-based task allocation approach for oversubscribed domains. In oversubscribed domains all tasks cannot be completed within the required deadlines due to a lack of resources. We focus specifically on domains where tasks can be generated throughout the mission, tasks can have different levels of importance and urgency, and penalties are assessed for failed commitments. Therefore, agents must reason about potential future events before making task commitments. Within these constraints, existing market-based approaches to task allocation can handle task importance and urgency, but do a poor job of anticipating future tasks, and are hence assessed a high number of penalties. In this work, we enhance a baseline market-based task allocation approach using regression-based learning to reduce overall incurred penalties. We illustrate the effectiveness of our approach in a simulated disaster response scenario by comparing performance with a baseline market-approach.

Keywords

multi-robot coordination, learning, disaster response

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Field Robotics Center

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

Associated Project(s): CTA Robotics

Text Reference

Edward Jones, M Bernardine Dias, and Anthony (Tony) Stentz, "Learning-enhanced market-based task allocation for oversubscribed domains," International Conference on Intelligent Robots and Systems, 2007. IROS 2007., November, 2007.

BibTeX Reference

@inproceedings{Jones_2007_6420,
   author = "Edward Jones and M Bernardine Dias and Anthony (Tony) Stentz",
   title = "Learning-enhanced market-based task allocation for oversubscribed domains",
   booktitle = "International Conference on Intelligent Robots and Systems, 2007. IROS 2007.",
   month = "November",
   year = "2007",
}

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