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.
Collective intelligence is generally defined as the emergence and evolution of intelligence derived from the collective and collaborative efforts of several entities; to include humans and (dis)embodied intelligent agents. Recent advances in immersive technology have led to cost-effective tools that allow us to study and replicate interactions in a controlled environment. Combined together, immersive collective intelligence holds the promise of a symbiotic intelligence that could be greater than the sum of the individual parts. For the military, where the decision making process is typically characterized by high-stress and high-consequence, the concept of a distributive, immersive collective intelligence capability is game changing. Commanders and staff will now be able to remotely immerse themselves in their operational environment with subject matter expertise and advanced analytics. This paper presents the initial steps to understanding immersive collective intelligence with a demonstration designed to discern how military intelligence analysts benefit from an immersive data visualization.
Visual analytics is a field of study which imparts knowledge through visual representations. The use of these visual representations provide a common method for analysts to sift through vast amounts of information and make informed decisions on critical matters. However, assisting the analyst in making connections with visual tools can be challenging if the information is not presented in an intuitive manner. This study aims to build upon our previous work and further investigate whether line thickness can be used as a valid visualization tool to improve situational awareness. In this paper, we follow-up on previous work to discuss research results exploring the impact that information complexity, measured as graph density, has on situational awareness. Our results indicate an increase in situational awareness, compared to non-enhanced visualizations for select graph densities. Furthermore, the results obtained in this study validate previous pilot study findings. The enhancement identified and validated with this research confirms that the line thickness visual cue represents a perceived information value tied to situational awareness. We conclude that this improved situational awareness and time savings occur from the decreased mental burden placed on the analyst.
The U.S. Army uses a standardized operation order (OPORD) for planning military operations. In this paper the U.S. Army Research Laboratory (ARL) considers using the OPORD as a basis for prioritizing information from the plethora of intelligence overwhelming an intelligence analyst. The OPORD would provide the input from which to calculate relevancy. To support this effort we review current approaches for calculating relevancy to improve existing information prioritization models, specifically value of information (VoI).
Modern military intelligence operations involves a deluge of information from a large number of sources. A data ranking
algorithm that enables the most valuable information to be reviewed first may improve timely and effective analysis.
This ranking is termed the value of information (VoI) and its calculation is a current area of research within the US
Army Research Laboratory (ARL). ARL has conducted an experiment to correlate the perceptions of subject matter
experts with the ARL VoI model and additionally to construct a cognitive model of the ranking process and the
amalgamation of supporting and conflicting information.
Wargaming is a process of thinking through and visualizing events that could occur during a possible course of action. Over the past 200 years, wargaming has matured into a set of formalized processes. One area of growing interest is the application of agent-based modeling. Agent-based modeling and its additional supporting technologies has potential to introduce a third-generation wargaming capability to the Army, creating a positive overmatch decision-making capability. In its simplest form, agent-based modeling is a computational technique that helps the modeler understand and simulate how the "whole of a system" responds to change over time. It provides a decentralized method of looking at situations where individual agents are instantiated within an environment, interact with each other, and empowered to make their own decisions. However, this technology is not without its own risks and limitations. This paper explores a technology roadmap, identifying research topics that could realize agent-based modeling within a tactical wargaming context.
Army staffs at division, brigade, and battalion levels often plan for contingency operations. As such, analysts consider the impact and potential consequences of actions taken. The Army Military Decision-Making Process (MDMP) dictates identification and evaluation of possible enemy courses of action; however, non-state actors often do not exhibit the same level and consistency of planned actions that the MDMP was originally designed to anticipate. The fourth MDMP step is a particular challenge, wargaming courses of action within the context of complex social-cultural behaviors. Agent-based Modeling (ABM) and its resulting emergent behavior is a potential solution to model terrain in terms of the human domain and improve the results and rigor of the traditional wargaming process.
This paper presents the concept development and demonstration of the Human Terrain Exploitation Suite (HTES) under development at the U.S. Army Research Laboratory’s Tactical Information Fusion Branch. The HTES is an amalgamation of four complementary visual analytic capabilities that target the exploitation of open source information. Open source information, specifically news feeds, blogs and other social media, provide a unique opportunity to collect and examine salient topics and trends. Analysis of open source information provides valuable insights into determining opinions, values, cultural nuances and other socio-political aspects within a military area of interest. The early results of the HTES field study indicate that the tools greatly increased the analysts’ ability to exploit open source information, but improvement through greater cross-tool integration and correlation of their results is necessary for further advances.
There has been significant progress recognizing the value of Intelligence, Surveillance, and Reconnaissance (ISR)
activities supporting Situational Awareness and Command and Control functions during the past several decades. We
consider ISR operations to be proactive (discovering activities or areas of interest), active (activities performed for a
particular task that flows down from a hierarchical process) or reactive (critical information gathering due to unexpected
events). ISR synchronization includes the analysis and prioritization of information requirements, identification of
intelligence gaps and the recommendation of available resources to gather information of interest, for all types of ISR
operations. It has become critically important to perform synchronized ISR activities to maximize the efficient
utilization of limited resources (both in quantity and capabilities) and, simultaneously, to increase the accuracy and
timeliness of the information gain. A study evaluating the existing technologies and processes supporting ISR activities
is performed suggesting a rigorous system optimization approach to the ISR synchronization process. Unfortunately,
this approach is not used today. The study identifies existing gaps between the current ISR synchronization process and
the proposed system optimization approach in the areas of communication and collaboration tools and advanced decision
aids (analytics). Solutions are recommended that will help close this gap.