The content discusses the potential of leveraging recent breakthroughs in interactive machine learning to enhance future Command and Control (C2) operations. It proposes three key research focus areas:
Developing human-AI interaction algorithms to enable robust planning in complex and dynamic environments. This includes methods for humans to guide learning and planning processes via various interaction modalities, techniques for multiple humans and AI agents to collaborate, and approaches that can operate effectively under limited communication and imperfect information.
Fostering resilient human-AI teams through optimizing roles, configurations, and trust. This involves defining resilient human-AI roles and team configurations, understanding how to select and train optimal human partners, and improving communication through explainable AI and shared mental models.
Scaling the approaches developed in the first two focus areas to succeed across a wide range of potential battlefield scenarios. This includes addressing temporal scalability, human-AI interaction scalability, hierarchical scalability, and problem sphere scalability.
Addressing these research gaps can enable the development of Scalable Interactive Machine Learning (SIML) systems that can revolutionize C2 operations by streamlining processes, maintaining coordination under denied/degraded conditions, and continuously adapting based on human feedback and evolving battlefield conditions.
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by Anna Madison... о arxiv.org 03-29-2024
https://arxiv.org/pdf/2402.06501.pdfГлибші Запити