Abstract
This research aims to develop and evaluate a real-time diagnosis assistant system that based on operator behavior recognition, hierarchical neural networks and expert explanation systems. The independent variables of were aiding levels and scenario types. The results showed that either partial or entire aiding approach could improve the performance significantly, and there was no significant difference between the partial aid and the entire aid. However, there was a significant effect of the level of aiding on the transitions of diagnostic strategy. These results revealed that with a direct and short information aiding, one could save diagnostic time and test steps.
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