A robust, distributed task allocation algorithm for time-critical, multi agent systems operating in uncertain environments
conference contributionposted on 31.03.2017 by Amanda Whitbrook, Qinggang Meng, Paul Chung
Any type of content contributed to an academic conference, such as papers, presentations, lectures or proceedings.
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.
This work was supported by EPSRC (grant number EP/J011525/1) with BAE Systems as the leading industrial partner.
- Computer Science