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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/24597

Title: A robust, distributed task allocation algorithm for time-critical, multi agent systems operating in uncertain environments
Authors: Whitbrook, Amanda
Meng, Qinggang
Chung, Paul Wai Hing
Issue Date: 2017
Publisher: © Springer
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.
Series/Report no.: Lecture Notes in Computer Science; 10351
Abstract: 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.
Description: This paper is in closed access until 03 June 2018.
Sponsor: This work was supported by EPSRC (grant number EP/J011525/1) with BAE Systems as the leading industrial partner.
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/24597
Publisher Link: http://www.cril.univ-artois.fr/ieaaie2017/
ISBN: 3319600443
Appears in Collections:Closed Access (Computer Science)

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