Reliable, distributed scheduling and rescheduling for time-critical, multiagent systems

This paper addresses two main problems with many heuristic task allocation approaches – solution trapping in local minima and static structure. The existing distributed task allocation algorithm known as PI (Performance Impact) is used as the vehicle for developing solutions to these problems as it has been shown to out-perform the state-of-the-art Consensus Based Bundle Algorithm (CBBA) for time-critical problems with tight deadlines, but is both static and sub-optimal with a tendency towards trapping in local minima. The paper describes two additional modules that are easily integrated with PI. The first extends the algorithm to permit dynamic online rescheduling in real time, and the second boosts performance by introducing an additional soft max action selection procedure that increases the algorithm’s exploratory properties. The paper demonstrates the effectiveness of the dynamic rescheduling module and shows that the average time taken to perform tasks can be reduced by up to 9% when the soft max module is used. In addition, the solution of some problems that baseline PI cannot handle is enabled by the second module. These developments represent a significant advance in the state-of-the-art for multi-agent, time-critical task assignment.

This paper addresses two main problems with many heuristic task allocation approaches – solution trapping in local minima and static structure. The existing distributed task allocation algorithm known as PI (Performance Impact) is used as the vehicle for developing solutions to these problems as it has been shown to out-perform the state-of-the-art Consensus Based Bundle Algorithm (CBBA) for time-critical problems with tight deadlines, but is both static and sub-optimal with a tendency towards trapping in local minima. The paper describes two additional modules that are easily integrated with PI. The first extends the algorithm to permit dynamic online rescheduling in real time, and the second boosts performance by introducing an additional soft max action selection procedure that increases the algorithm’s exploratory properties. The paper demonstrates the effectiveness of the dynamic rescheduling module and shows that the average time taken to perform tasks can be reduced by up to 9% when the soft max module is used. In addition, the solution of some problems that baseline PI cannot handle is enabled by the second module. These developments represent a significant advance in the state-of-the-art for multi-agent, time-critical task assignment.

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