Bidirectional Pilot Intent Translation for Human-Machine Dialog
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Solution Overview
Problem
Current human-machine interfaces in aeronautics face challenges in understanding pilot intentions, leading to risky situations due to the complexity of translating high-level intentions into machine-readable instructions, especially under severe time constraints and cognitive load, with limited satisfactory solutions in the scientific and patent literature.
Innovation Solution
The development of bidirectional communication systems using universal approximators for bottom-up and top-down translation functions, integrated with fuzzy logic and machine learning algorithms, to capture and translate pilot intentions into machine-understandable language and vice versa, reducing the semantic gap between human and machine abstraction levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the pilot translates intentions into technical terms for machine control, then the machine can execute commands, but the cognitive load and time required increase significantly
Solution Approach 1:
The patent introduces an intermediary translation system that includes a bottom-up translator (neural network) and a top-down translator (fuzzy logic system). These intermediaries automatically convert between pilot intentions and machine commands, eliminating the need for the pilot to manually translate intentions into technical terms, thus reducing cognitive load and translation time.
Solution Approach 2:
The system implements bidirectional feedback loops where the bottom-up translator learns from pilot inputs and the top-down translator provides feedback to refine intention recognition. This continuous feedback mechanism improves the accuracy and speed of translation over time, further reducing the time loss associated with intention-to-command conversion.
2Measurement precision
If the system uses complex translation algorithms, then translation accuracy improves, but device complexity increases
Solution Approach 1:
The translation system is segmented into two distinct modules: a bottom-up translator using neural networks for pattern recognition, and a top-down translator using fuzzy logic for rule-based reasoning. This segmentation allows each module to specialize in specific aspects of translation, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The system combines different computational approaches (neural networks and fuzzy logic) into a composite translation architecture. This composite structure leverages the strengths of both methods - the pattern recognition capability of neural networks and the interpretability of fuzzy logic - achieving high translation accuracy while maintaining manageable system complexity through complementary techniques.
3Productivity
If the pilot manages multiple systems under time constraints, then operational efficiency may improve, but the risk of errors increases
Solution Approach 1:
The translation system operates autonomously without requiring pilot intervention. It automatically recognizes pilot intentions, translates them into appropriate machine commands, and executes the translation in real-time. This self-service capability allows the pilot to focus on high-level decision-making while the system handles the complex translation tasks, improving both efficiency and reliability.
Data Source
AI summary
Systems and methods for improved human-machine dialog, include bidirectional translations notably through the translation of commands by the human into a form able to be manipulated by the machine, and conversely of results produced by the machine into a form intelligible to the human. Some developments describe notably the display of portions of intermediate reasoning followed by the machine (for example explanation of root causes).