A novel distributed scheduling algorithm for time-critical multi-agent systems

This paper describes enhancements made to the distributed performance impact (PI) algorithm and presents the results of trials that show how the work advances the stateof- the-art in single-task, single-robot, time-extended, multiagent task assignment for time-critical missions. The improvement boosts performance by integrating the architecture with additional action selection methods that increase the exploratory properties of the algorithm (either soft max or ε-greedy task selection). It is demonstrated empirically that the average time taken to perform rescue tasks can reduce by up to 8% and solution of some problems that baseline PI cannot handle is enabled. Comparison with the consensusbased bundle algorithm (CBBA) also shows that both the baseline PI algorithm and the enhanced versions are superior. All test problems center around a team of heterogeneous, autonomous vehicles conducting rescue missions in a 3- dimensional environment, where a number of different tasks must be carried out in order to rescue a known number of victims that is always more than the number of available vehicles.