Abstract
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.
Highlights
The ability to assign tasks well in the light of intrinsic uncertainty is very valuable for multi-agent task allocation systems
Pursuing choices with the highest expected value is sensible when the payoff appears to be both high and probable, but the worst-case option makes more sense when the payoff might be lower and less likely. As both situations may be encountered in the type of task allocation problems considered, a third candidate (C) is created, which uses a hybrid combination of the expected value and worstcase scenario metrics, in a bid to match the time cost estimates with the degree of risk, and improve overall robustness
Baseline Performance Impact algorithm (PI) does not handle uncertainty well; in all three experiment sets with the baseline, a high percentage of the solutions fail to allocate all of the tasks when simulated uncertainty is high, and the number of unallocated tasks is relatively large
Summary
The ability to assign tasks well in the light of intrinsic uncertainty is very valuable for multi-agent task allocation systems. To date, centralized systems have dominated research focus This is not surprising since distributed task allocation for multi-agent systems operating in uncertain environments is a challenging problem [2]. Pursuing choices with the highest expected value is sensible when the payoff appears to be both high and probable (task deadlines are not so tight), but the worst-case option makes more sense when the payoff might be lower and less likely (there are some tight task deadlines) As both situations may be encountered in the type of task allocation problems considered, a third candidate (C) is created, which uses a hybrid combination of the expected value and worstcase scenario metrics, in a bid to match the time cost estimates with the degree of risk, and improve overall robustness
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