An Emergency Disposal Decision-making Method with Human--Machine Collaboration

• URL: http://arxiv.org/abs/2305.18165v1
• Date: Mon, 29 May 2023 15:53:20 GMT
• Title: An Emergency Disposal Decision-making Method with Human--Machine Collaboration
• Abstract summary: In modern operational environments, all job plans are affected by emergency events such as equipment failures and resource shortages. The use of unmanned systems to assist decision-making can improve resolution efficiency, but their decision-making is not interpretable. We propose a collaborative human--machine method for resolving unplanned events using two phases: task filtering and task scheduling.
• Score: 20.84526084548098
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rapid developments in artificial intelligence technology have led to unmanned systems replacing human beings in many fields requiring high-precision predictions and decisions. In modern operational environments, all job plans are affected by emergency events such as equipment failures and resource shortages, making a quick resolution critical. The use of unmanned systems to assist decision-making can improve resolution efficiency, but their decision-making is not interpretable and may make the wrong decisions. Current unmanned systems require human supervision and control. Based on this, we propose a collaborative human--machine method for resolving unplanned events using two phases: task filtering and task scheduling. In the task filtering phase, we propose a human--machine collaborative decision-making algorithm for dynamic tasks. The GACRNN model is used to predict the state of the job nodes, locate the key nodes, and generate a machine-predicted resolution task list. A human decision-maker supervises the list in real time and modifies and confirms the machine-predicted list through the human--machine interface. In the task scheduling phase, we propose a scheduling algorithm that integrates human experience constraints. The steps to resolve an event are inserted into the normal job sequence to schedule the resolution. We propose several human--machine collaboration methods in each phase to generate steps to resolve an unplanned event while minimizing the impact on the original job plan.

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