Soldiers are often required to do missions that may take
many hours and are quite stressful.
Soldiers are requested to write a report detailing the most
significant events that occurred once a mission is completed.
This report is designed to collect information about the
environment and local/foreign people in order to better organize future
operations.
Soldiers often offer this report primarily based on their
memories, still photographs, and GPS data from portable equipment.
There are probably numerous cases when crucial information
is missing and not accessible for future mission planning due to the severe
stress they face.
Soldiers were equipped with sensors that could be worn
directly on their uniforms as part of the ASSIST (Advanced Soldier Sensor
Information Systems and Technology) program, which addressed this problem.
Sensors continually recorded what was going on around the
troops during the operation.
When the troops returned from their mission, the sensor data
was indexed and an electronic record of the events that occurred while the
ASSIST system was recording was established.
Soldiers might offer more accurate reports if they had this
knowledge instead of depending simply on their memories.
Numerous functions were made possible by AI-based
algorithms, including:
• "Capabilities for Image/Video Data Analysis"
• Object Detection/Image Classification—the capacity to
detect and identify items (such as automobiles, persons, and license plates)
using video, images, and/or other data sources.
• "Audio Data Analysis Capabilities"
• "Arabic Text Translation"—the ability to detect,
recognize, and translate written Arabic text (e.g., in imagery data)
• "Change Detection"—the ability to detect changes
in related data sources over time (e.g., identify differences in imagery of the
same location at different times)
• Sound Recognition/Speech Recognition—the capacity to
distinguish speech (e.g., keyword spotting and foreign language recognition)
and identify sound events (e.g., explosions, gunfire, and cars) in audio data.
• Shooter Localization/Shooter Classification—the ability to
recognize gunshots in the environment (e.g., via audio data processing), as
well as the kind of weapon used and the shooter's position.
• "Capabilities for Soldier Activity Data
Analysis"
• Soldier State Identification/Soldier Localization—the
capacity to recognize a soldier's course of movement in a given area and
characterize the soldier's activities (e.g., running, walking, and climbing
stairs) To be effective, AI systems like this (also known as autonomous or
intelligent systems) must be thoroughly and statistically analyzed to verify
that they would work correctly and as intended in a military setting.
The National Institute of Standards and Technology (NIST)
was entrusted with assessing these AI systems using three criteria:
1. The precision with which objects, events, and activities
are identified and labeled
2. The system's capacity to learn and improve its
categorization performance.
3. The system's usefulness in improving operational
efficiency To create its performance measurements, NIST devised a two-part test
technique.
Metrics 1 and 2 were assessed using component- and
system-level technical performance evaluations, respectively, while meter 3 was
assessed using system-level utility assessments.
The utility assessments were created to estimate the effect
these technologies would have on warfighter performance in a range of missions
and job tasks, while the technical performance evaluations were created to
ensure the ongoing improvement of ASSIST system technical capabilities.
NIST endeavored to create assessment techniques that would
give an appropriate degree of difficulty for system and soldier performance
while defining the precise processes for each sort of evaluation.
The ASSIST systems were divided down into components that
implemented certain capabilities at the component level.
For example, the system was divided down into an Arabic text
identification component, an Arabic text extraction component (to localize
individual text characters), and a text translation component to evaluate its
Arabic translation capacity.
Each factor was evaluated on its own to see how it affected
the system.
Each ASSIST system was assessed as a black box at the system
level, with the overall performance of the system being evaluated independently
of the individual component performance.
The total system received a single score, which indicated
the system's ability to complete the overall job.
A test was also conducted at the system level to determine
the system's usefulness in improving operational effectiveness for
after-mission reporting.
Because all of the systems reviewed as part of this
initiative were in the early phases of development, a formative assessment
technique was suitable.
NIST was especially interested in determining the system's
value for warfighters.
As a result, we were worried about the influence on their procedures
and goods.
User-centered metrics were used to represent this viewpoint.
NIST set out to find measures that may assist answer questions
like: What information do infantry troops seek and/or require after completing
a field mission? From both the troops' and the S2's (Staff 2—Intelligence
Officer) perspectives, how successfully are information demands met? What was
ASSIST's contribution to mission reporting in terms of user-stated information
requirements? This examination was carried out at the Aberdeen Test Center
Military Operations in Urban Terrain (MOUT) location in Aberdeen, Maryland.
The location was selected for a variety of reasons:
• Ground truth—Aberdeen was able to deliver ground truth to
within two centimeters of chosen locations.
This provided a strong standard against which the system
output could be compared, enabling the assessment team to get a good depiction
of what really transpired in the environment.
• Realism—The MOUT location has around twenty structures
that were built up to seem like an Iraqi town.
• Testing infrastructure—The facility was outfitted with a
number of cameras (both inside and outside) to help us better comprehend the
surroundings during testing.
• Soldier availability—For the assessment, the location was
able to offer a small squad of active-duty troops.
The MOUT site was enhanced with items, people, and
background noises whose location and behavior were programmed to provide a more
operationally meaningful test environment.
The goal was to provide an environment in which the various
ASSIST systems could test their capabilities by detecting, identifying, and/or
capturing various forms of data.
Foreign language speech detection and classification, Arabic
text detection and recognition, detection of shots fired and vehicle sounds,
classification of soldier states and tracking their locations (both inside and
outside of buildings), and identifying objects of interest such as vehicles,
buildings, people, and so on were all included in NIST's utility assessments.
Because the tests required the troops to respond according
to their training and experience, the soldiers' actions were not scripted as they
progressed through each exercise.
~ Jai Krishna Ponnappan
You may also want to read more about Artificial Intelligence here.
See also: Battlefield AI and Robotics; Cybernetics and AI.
Further Reading
Schlenoff, Craig, Brian Weiss, Micky Steves, Ann Virts, Michael Shneier, and Michael Linegang. 2006. “Overview of the First Advanced Technology Evaluations for ASSIST.” In Proceedings of the Performance Metrics for Intelligence Systems Workshop, 125–32. Gaithersburg, MA: National Institute of Standards and Technology.
Steves, Michelle P. 2006. “Utility Assessments of Soldier-Worn Sensor Systems for ASSIST.” In Proceedings of the Performance Metrics for Intelligence Systems Workshop, 165–71. Gaithersburg, MA: National Institute of Standards and Technology.
Washington, Randolph, Christopher Manteuffel, and Christopher White. 2006. “Using an Ontology to Support Evaluation of Soldier-Worn Sensor Systems for ASSIST.” In Proceedings of the Performance Metrics for Intelligence Systems Workshop, 172–78. Gaithersburg, MA: National Institute of Standards and Technology.
Weiss, Brian A., Craig I. Schlenoff, Michael O. Shneier, and Ann Virts. 2006. “Technology Evaluations and Performance Metrics for Soldier-Worn Sensors for ASSIST.” In Proceedings of the Performance Metrics for Intelligence Systems Workshop, 157–64. Gaithersburg, MA: National Institute of Standards and Technology.
