Proc. SPIE. 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
KEYWORDS: Mathematical modeling, Statistical analysis, Data modeling, Visualization, Data processing, Machine learning, Analytical research, Data communications, Performance modeling, Data integration, Strategic intelligence, Military intelligence
Army Intelligence operates in a data rich environment with limited ability to operationalize exponentially increasing volumes of disparate structured and unstructured data to deliver timely, accurate, relevant, and tailored intelligence in support of mission command at echelon. The volume, velocity, variety, and veracity (the 4 Vs) of data challenge existing Army intelligence systems and processes, degrading the efficacy of the Intelligence Warfighting Function (IWfF). At the same time, industry has exploited the recent growth in data science technology to address the challenge of the 4 Vs and bring relevant data-driven insights to business leaders. To bring together the lessons from industry and the data science community, the US Army Research Laboratory (ARL) has collaborated with the US Army Intelligence Center of Excellence (USAICoE) to research these Military Intelligence (MI) challenges in an Army AR 5-5 Study entitled, “Application of Data Science within the Army Intelligence Warfighting Function.” This paper summarizes the problem statement, research performed, key findings, and way forward for MI to effectively employ data science and data scientists to reduce the burden on Army Intelligence Analysts and increase the effectiveness of data exploitation to maintain a competitive edge over our adversaries.
Live sensor data was obtained from an Open Standard for Unattended Sensors (OSUS, formerly Terra Harvest)- based system provided by the Army Research Lab (ARL) and fed into the Communications-Electronics Research, Development and Engineering Center (CERDEC) sponsored Actionable Intelligence Technology Enabled Capabilities Demonstration (AI-TECD) Micro Cloud during the E15 demonstration event that took place at Fort Dix, New Jersey during July 2015. This data was an enabler for other technologies, such as Sensor Assignment to Mission (SAM), Sensor Data Server (SDS), and the AI-TECD Sensor Dashboard, providing rich sensor data (including images) for use by the Company Intel Support Team (CoIST) analyst. This paper describes how the OSUS data was integrated and used in the E15 event to support CoIST operations.
Sensor-mission assignment involves the allocation of sensors and other information-providing resources to missions in order to cover the information needs of the individual tasks within each mission. The importance of efficient and effective means to find appropriate resources for tasks is exacerbated in the coalition context where the operational environment is dynamic and a multitude of critically important tasks need to achieve their collective goals to meet the objectives of the coalition. The Sensor Assignment to Mission (SAM) framework—a research product of the International Technology Alliance in Network and Information Sciences (NIS-ITA) program—provided the first knowledge intensive resource selection approach for the sensor network domain so that contextual information could be used to effectively select resources for tasks in coalition environments. Recently, CUBRC, Inc. was tasked with operationalizing the SAM framework through the use of the I2WD Common Core Ontologies for the Communications-Electronics Research, Development and Engineering Center (CERDEC) sponsored Actionable Intelligence Technology Enabled Capabilities Demonstration (AI-TECD). The demonstration event took place at Fort Dix, New Jersey during July 2015, and this paper discusses the integration and the successful demonstration of the SAM framework within the AI-TECD, lessons learned, and its potential impact in future operations.
In the Army Intelligence domain, Processing, Exploitation, and Dissemination (PED) is the process that is used to convert and collect information into actionable intelligence and then distribute this intelligence appropriately to those who make decisions and execute the tasks and missions that this intelligence process supports. In today’s Intelligence domain, information is gathered from an abundance of sources and these sensors create an exponential amount of data output. PED is a time sensitive process, which is also constrained by manpower and the extremely limited tactical bandwidth. Currently, PED is primarily a higher echelon activity, but as information gathering increases at the platforms it makes sense to automate PED tasks and execute these tasks closer to the sensor sources. Providing an architecture that will allow for processing sensor data more intelligently at various locations within the Intel network to include: on-board a UAV or vehicle, COIST, and higher echelons can help to alleviate these constraints by positioning the sensor fusion as close as possible to minimize bandwidth utilization. However, this architecture will implicitly need a way to share data to enable fusion. While any given mission may require fusion of just a few sensor data sources, which can be accomplished with point-to-point integration, this approach does not scale and is not maintainable, since the range of all missions will require a combination of any number of data sources and this approach will most likely require extra development to handle new sources. Therefore, there needs to be a way to share and reuse date that is extensible, maintainable, and not tied to any one mission type. This approach will reduce duplication, provide common patterns for accessing information and support future growth. This paper describes how a common ontology can be used to transform intelligence data from any number of disparate sources to a higher level of integration where one uses the logical understanding of the domain to share knowledge between sources. This paper will discuss sensor ontology efforts to date, introduce the Common Core Ontologies which provide the common upper and mid-level semantics which are inherited by domain level ontologies and describe future experimentation. The paper will discuss the role of the Common Core Ontologies development and governance practices in producing a logically consistent data set, which can be accessed through a single API. By utilizing this approach, sensor outputs can be fused using inferencing, entity and event resolution, and other 3rd party analytic apps. Finally, the paper will also describe how ontologies are leveraged to enable tasking, analytics, rules based reasoning, and distributed processing which are functional components currently being utilized or developed to support the PED process.