Practice Makes Perfect: Lesson Learned from Five Years of Trial and Error Building Context-Aware Systems

Mullins, Ryan
Fouse, Adam
Ganberg, Gabriel
Schurr, Nathan
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Recent advances in artificial intelligence have demonstrated that the future of work will be defined by collaborative human-machine teams. In order to be effective, human-machine teams will rely on context-aware systems to enable collaboration. In this paper, we present three lessons learned from the past five years of developing context-aware systems that we believe will improve future system design. First, that semantic activity must captured, modeled, and analyzed to enable reasoning across missions, actors, and content. Second, that context-aware systems require multiple, federated data stores to optimize system and team performance. Finally, that real-time inter-actor communications are the essential feature enabling adaptation. We close with a discussion of the influences and implications that these lessons have on human-machine teaming, and outline future research activities that will be necessary before operationalizing these systems.
Collaboration with Automation: Machines as Teammates, context-aware systems, decision-support systems, human-machine collaboration, lessons learned
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