2023/05/29 by Yibo Guo, Guo, Yibo, Jingyi Xue +5
Decision Sciences · Health Professions · Psychology · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Human-Automation Interaction and Safety #Human-Computer Interaction (cs.HC) #Occupational Health and Safety Research #Risk and Safety Analysis
paper · pdf · doi:10.48550/arxiv.2305.18165
openalex publication_date 2023/05/29 · openalex created_date 2023/05/31 · openalex updated_date 2026/07/28
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.