Context-Driven Proactive Decision Support for Hybrid Teams

Authors

  • Manisha Mishra Aptiv Corporation
  • Pujitha Mannaru University of Connecticut
  • David Sidoti U.S. Naval Research Laboratory
  • Adam Bienkowski University of Connecticut
  • Lingyi Zhang University of Connecticut
  • Krishna R. Pattipati University of Connecticut

DOI:

https://doi.org/10.1609/aimag.v40i3.4810

Abstract

A synergy between AI and the Internet of Things (IoT) will significantly improve sense-making, situational awareness, proactivity, and collaboration. However, the key challenge is to identify the underlying context within which humans interact with smart machines. Knowledge of the context facilitates proactive allocation among members of a human–smart machine (agent) collective that balances auto­nomy with human interaction, without displacing humans from their supervisory role of ensuring that the system goals are achievable. In this article, we address four research questions as a means of advancing toward proactive autonomy: how to represent the interdependencies among the key elements of a hybrid team; how to rapidly identify and characterize critical contextual elements that require adaptation over time; how to allocate system tasks among machines and agents for superior performance; and how to enhance the performance of machine counterparts to provide intelligent and proactive courses of action while considering the cognitive states of human operators. The answers to these four questions help us to illustrate the integration of AI and IoT applied to the maritime domain, where we define context as an evolving multidimensional feature space for heterogeneous search, routing, and resource allocation in uncertain environments via proactive decision support systems.

Additional Files

Published

2019-07-09

How to Cite

Mishra, M., Mannaru, P., Sidoti, D., Bienkowski, A., Zhang , L., & Pattipati, K. (2019). Context-Driven Proactive Decision Support for Hybrid Teams. AI Magazine, 40(3), 41-57. https://doi.org/10.1609/aimag.v40i3.4810

Issue

Vol. 40 No. 3: Fall 2019

Section

Articles

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