Intelligent systems for aviation need to be capable of understanding and representing anomalous events as they happen in real-time. We explore this problem with a proof of concept framework based on contextual one-shot learning, run on a human-in-the-loop flight simulator. We ran a total of 24 trials, with variations in training, fliers, and set values within the framework, and found that our framework was able to detect and reason about anomalies in all trials. In future work, we would like to explore different heuristics for anomaly reasoning, including nonlinear interactions of cockpit data, and feedback from the flight crew through psychophysiology sensors or natural language interactions.
Knowledge Acquisition in the Cockpit Using One-Shot Learning
2018-07-01
2252008 byte
Conference paper
Electronic Resource
English
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