Dynamically Managing Task Allocation Between Humans and Machines in Surveillance Operations

Constructing an Autonomous Manager (AM) for use as an integral component of distributing multiple tasks between humans and autonomous agents, particularly in Intelligence, Surveillance, and Reconnaissance (ISR) applications.

Air Force Research Laboratory, Wright-Patterson Air Force Base, Ohio

Increasingly sophisticated technology must be leveraged in surveillance environments to enable eventually achieving the goal of allowing analysts to increase throughput by managing multiple simultaneous feeds. Maintaining this increased tasking will likely introduce additional workload and fatigue. Fortunately, analysts can currently offload some of these tasks to automation and will, in the future, be able to offload additional tasking to streamline the intelligence analysis process.

Currently, various speech-to-text and text-to-speech programs can be used to convert spoken information into chat and automation can be used to copy text to multiple needed locations simultaneously. Automation has aided in the transmission of information between analysts and organizations. Tools are also being developed to augment the detection of important visual features within surveillance scenes. However, the degree of assistance autonomous systems can provide is still somewhat limited for cognitively complex tasks, but progress is being made incrementally toward viable assistive tools. Balancing analyst workload while maintaining multiple tasks will require intelligent and dynamic distribution of tasks between humans and autonomy.