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A robust, distributed task allocation algorithm for time-critical, multi agent systems operating in uncertain environments

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conference contribution
posted on 31.03.2017 by Amanda Whitbrook, Qinggang Meng, Paul Chung
The aim of this work is to produce and test a robust, distributed, mul-ti-agent task allocation algorithm, as these are scarce and not well-documented in the literature. The vehicle used to create the robust system is the Performance Impact algorithm (PI), as it has previously shown good performance. Three dif-ferent variants of PI are designed to improve its robustness, each using Monte Carlo sampling to approximate Gaussian distributions. Variant A uses the ex-pected value of the task completion times, variant B uses the worst-case scenar-io metric and variant C is a hybrid that implements a combination of these. The paper shows that, in simulated trials, baseline PI does not handle uncertainty well; the task-allocation success rate tends to decrease linearly as degree of un-certainty increases. Variant B demonstrates a worse performance and variant A improves the failure rate only slightly. However, in comparison, the hybrid var-iant C exhibits a very low failure rate, even under high uncertainty. Further-more, it demonstrates a significantly better mean objective function value than the baseline.

Funding

This work was supported by EPSRC (grant number EP/J011525/1) with BAE Systems as the leading industrial partner.

History

School

  • Science

Department

  • Computer Science

Published in

The 30th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems

Citation

WHITBROOK, A., MENG, Q. and CHUNG, P.W.H., 2017. A robust, distributed task allocation algorithm for time-critical, multi agent systems operating in uncertain environments. IN: Benferhat, S., Tabia, K. and Ali, M. (eds.) Advances in Artificial Intelligence: From Theory to Practice. 30th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE 2017), Arras, France, June 27-30th, Proceedings, Part II, pp 55-64.

Publisher

© Springer

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

21/02/2017

Publication date

2017

Notes

This is a pre-copyedited version of a contribution published in Benferhat, S., Tabia, K. and Ali, M. (eds.) Advances in Artificial Intelligence: From Theory to Practice. 30th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE 2017) published by Springer. The definitive authenticated version is available online via https://doi.org/10.1007/978-3-319-60045-1_8

ISBN

3319600443;9783319600444

Book series

Lecture Notes in Computer Science; 10351

Language

en

Location

France

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