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Addressing robustness in time-critical, distributed, task allocation algorithms

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journal contribution
posted on 30.05.2018, 08:30 by Amanda Whitbrook, Qinggang MengQinggang Meng, Paul Chung
The aim of this work is to produce and test a robustness module (ROB-M) that can be generally applied to distributed, multi-agent task allocation algorithms, as robust versions of these are scarce and not well-documented in the literature. ROB-M is developed using the Performance Impact (PI) algorithm, as this has previously shown good results in deterministic trials. Different candidate versions of the module are thus bolted on to the PI algorithm and tested using two different task allocation problems under simulated uncertain conditions, and results are compared with baseline PI. It is shown that the baseline does not handle uncertainty well; the task-allocation success rate tends to decrease linearly as degree of uncertainty increases. However, when PI is run with one of the candidate robustness modules, the failure rate becomes very low for both problems, even under high simulated uncertainty, and so its architecture is adopted for ROB-M and also applied to MIT’s baseline Consensus Based Bundle Algorithm (CBBA) to demonstrate its flexibility. Strong evidence is provided to show that ROB-M can work effectively with CBBA to improve performance under simulated uncertain conditions, as long as the deterministic versions of the problems can be solved with baseline CBBA. Furthermore, the use of ROB-M does not appear to increase mean task completion time in either algorithm, and only 100 Monte Carlo samples are required compared to 10,000 in MIT’s robust version of the CBBA algorithm. PI with ROB-M is also tested directly against MIT’s robust algorithm and demonstrates clear superiority in terms of mean numbers of solved tasks.

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

Applied Intelligence

Pages

1 - 15

Citation

WHITBROOK, A., MENG, Q. and CHUNG, P.W.H., 2018. Addressing robustness in time-critical, distributed, task allocation algorithms. Applied Intelligence, 49 (1), pp.1–15.

Publisher

Springer © The Author(s)

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/

Acceptance date

05/03/2018

Publication date

2018-04-18

Notes

This is an Open Access Article. It is published by Springer under the Creative Commons Attribution 4.0 International License (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

ISSN

0924-669X

eISSN

1573-7497

Language

en