Mutual Understanding in Human-Machine Teaming
Keywords:Human-Machine Teaming, Personalized Machine Learning, Explainable AI, Learning From Demonstration, Multi-Agent Systems
AbstractCollaborative robots (i.e., "cobots") and machine learning-based virtual agents are increasingly entering the human workspace with the aim of increasing productivity, enhancing safety, and improving the quality of our lives. These agents will dynamically interact with a wide variety of people in dynamic and novel contexts, increasing the prevalence of human-machine teams in healthcare, manufacturing, and search-and-rescue. In this research, we enhance the mutual understanding within a human-machine team by enabling cobots to understand heterogeneous teammates via person-specific embeddings, identifying contexts in which xAI methods can help improve team mental model alignment, and enabling cobots to effectively communicate information that supports high-performance human-machine teaming.
How to Cite
Paleja, R. (2022). Mutual Understanding in Human-Machine Teaming. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12896-12897. https://doi.org/10.1609/aaai.v36i11.21585
The Twenty - Seventh AAAI / SIGAI Doctoral Consortium