A Robust, Distributed Task Allocation Algorithm for Time-Critical, Multi Agent Systems Operating in Uncertain Environments

The 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 handle uncertainty well; the task-allocation success rate tends to decrease linearly as degree of uncertainty increases. Variant B demonstrates a worse performance 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 objective function value than the baseline.