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dc.contributor.authorWhitbrook, Amanda
dc.contributor.authorMeng, Qinggang
dc.contributor.authorChung, Paul W. H.
dc.date.accessioned2017-05-11T08:32:28Z
dc.date.available2017-05-11T08:32:28Z
dc.date.issued2017-06-27
dc.identifier.citationWhitbrook, 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 Proceedings, 30th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems(IEA/AIE 2017), Arras, France, June 17- 21.en
dc.identifier.urihttp://hdl.handle.net/10545/621611
dc.description.abstractThe aim of this work is to produce and test a robust, distributed, multi-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 different variants of PI are designed to improve its robustness, each using Monte Carlo sampling to approximate Gaussian distributions. Variant A uses the expected value of the task completion times, variant B uses the worst-case scenario metric and variant C is a hybrid that implements a combination of these. The paper shows that, in simulated trials, baseline PI does not han-dle uncertainty well; the task-allocation success rate tends to decrease linear-ly as degree of uncertainty increases. Variant B demonstrates a worse per-formance and variant A improves the failure rate only slightly. However, in comparison, the hybrid variant C exhibits a very low failure rate, even under high uncertainty. Furthermore, it demonstrates a significantly better mean ob-jective function value than the baseline.
dc.description.sponsorshipEPSRCen
dc.language.isoenen
dc.relation.urlhttp://www.cril.univ-artois.fr/ieaaie2017/en
dc.subjectMulti-agent systemsen
dc.subjectDistributed task allocationen
dc.subjectAuction-based schedulingen
dc.subjectRobustness to uncertaintyen
dc.titleA robust, distributed task allocation algorithm for time-critical, multi agent systems operating in uncertain environmentsen
dc.typeMeetings and Proceedingsen
dc.contributor.departmentUniversity of Derbyen
dc.contributor.departmentLoughborough Universityen
refterms.dateFOA2019-02-28T15:45:40Z
html.description.abstractThe aim of this work is to produce and test a robust, distributed, multi-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 different variants of PI are designed to improve its robustness, each using Monte Carlo sampling to approximate Gaussian distributions. Variant A uses the expected value of the task completion times, variant B uses the worst-case scenario metric and variant C is a hybrid that implements a combination of these. The paper shows that, in simulated trials, baseline PI does not han-dle uncertainty well; the task-allocation success rate tends to decrease linear-ly as degree of uncertainty increases. Variant B demonstrates a worse per-formance and variant A improves the failure rate only slightly. However, in comparison, the hybrid variant C exhibits a very low failure rate, even under high uncertainty. Furthermore, it demonstrates a significantly better mean ob-jective function value than the baseline.


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