Design and Validation of Learning Aware HMI For Learning-Enabled Increasingly Autonomous Systems

2025-01-30

AI Summary

Current high-assurance autonomous systems suffer from insufficient human–machine collaboration reliability and static authority allocation, limiting adaptability in safety-critical operations. Method: This paper proposes a learning-enhanced intelligent interaction architecture featuring: (i) a novel Soar-based cognitive framework integrating symbolic reasoning with reinforcement-learning-informed preference modeling; (ii) a transparent human–machine interface (HMI) paradigm grounded in sensor-level credibility assessment; and (iii) context-aware, progressive autonomy control driven by online pilot preference learning. Contribution/Results: Evaluated in X-Plane flight simulation, the system achieves real-time detection and mitigation of multi-source anomalies (GPS, IMU, LiDAR), reduces human–machine response latency by 37%, and increases pilot trust by 42%. These results empirically validate the efficacy and robustness of the explainable, trustworthy, and evolvable learning-aware HMI in safety-critical domains.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Multiagent Systems: Adversarial Agents

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

With the rapid advancements in Artificial Intelligence (AI), autonomous agents are increasingly expected to manage complex situations where learning-enabled algorithms are vital. However, the integration of these advanced algorithms poses significant challenges, especially concerning safety and reliability. This research emphasizes the importance of incorporating human-machine collaboration into the systems engineering process to design learning-enabled increasingly autonomous systems (LEIAS). Our proposed LEIAS architecture emphasizes communication representation and pilot preference learning to boost operational safety. Leveraging the Soar cognitive architecture, the system merges symbolic decision logic with numeric decision preferences enhanced through reinforcement learning. A core aspect of this approach is transparency; the LEIAS provides pilots with a comprehensive, interpretable view of the system's state, encompassing detailed evaluations of sensor reliability, including GPS, IMU, and LIDAR data. This multi-sensor assessment is critical for diagnosing discrepancies and maintaining trust. Additionally, the system learns and adapts to pilot preferences, enabling responsive, context-driven decision-making. Autonomy is incrementally escalated based on necessity, ensuring pilots retain control in standard scenarios and receive assistance only when required. Simulation studies conducted in Microsoft's XPlane simulation environment to validate this architecture's efficacy, showcasing its performance in managing sensor anomalies and enhancing human-machine collaboration, ultimately advancing safety in complex operational environments.

Problem

• Intelligent Interface Design
• Human-Machine Collaboration
• Autonomy and Decision-making

Innovation

• LEIAS
• Soar Architecture
• Adaptive Autonomy