The ability to collect an ever-increasing amount of information is outpacing analysts’ ability to interpret and communicate that information in a timely manner. The Army Research Laboratory’s (ARL’s) Signal and Image Processing (SIP) Division is presently engaged in research to develop a system-of-systems methodology designed around a Mission & Means Framework (MMF), a robust, rapid-reaction, autonomous information-generating tool that can provide the mission-relevant information/intelligence that Commanders need to make winning decisions on the battlefield. The MMF provides a structure that enables the optimal allocation of available information sources to capture and exploit Mission-Informed Needed Information based on Discoverable, Available Sensing Sources (MINI-DASS). In this paper, we describe an MMF operator that matches information needs to information means using ontologies that describe both the information requirements and the information sources. We then describe two different multi-objective optimization techniques to effectively explore the large, complex search space o possible matches to discover suitable solutions that match available information-source means to satisfy mission needs.
The U.S. Air Force is consistently evolving to support current and future operations through the planning and execution
of intelligence, surveillance and reconnaissance (ISR) missions. However, it is a challenge to maintain a precise
awareness of current and emerging ISR capabilities to properly prepare for future conflicts. We present a decisionsupport
tool for acquisition managers to empirically compare ISR capabilities and approaches to employing them,
thereby enabling the DoD to acquire ISR platforms and sensors that provide the greatest return on investment. We have
developed an analysis environment to perform modeling and simulation-based experiments to objectively compare
alternatives. First, the analyst specifies an operational scenario for an area of operations by providing terrain and threat
information; a set of nominated collections; sensor and platform capabilities; and processing, exploitation, and
dissemination (PED) capacities. Next, the analyst selects and configures ISR collection strategies to generate collection
plans. The analyst then defines customizable measures of effectiveness or performance to compute during the
experiment. Finally, the analyst empirically compares the efficacy of each solution and generates concise reports to
document their conclusions, providing traceable evidence for acquisition decisions. Our capability demonstrates the
utility of using a workbench environment for analysts to design and run experiments. Crafting impartial metrics enables
the acquisition manager to focus on evaluating solutions based on specific military needs. Finally, the metric and
collection plan visualizations provide an intuitive understanding of the suitability of particular solutions. This facilitates
a more agile acquisition strategy that handles rapidly changing technology in response to current military needs.
To support an Effects Based Approach to Operations (EBAO), Intelligence, Surveillance, and Reconnaissance (ISR)
planners must optimize collection plans within an evolving battlespace. A need exists for a decision support tool that
allows ISR planners to rapidly generate and rehearse high-performing ISR plans that balance multiple objectives and
constraints to address dynamic collection requirements for assessment. To meet this need we have designed an
evolutionary algorithm (EA)-based "Integrated ISR Plan Analysis and Rehearsal System" (I2PARS) to support Effects-based
Assessment (EBA). I2PARS supports ISR mission planning and dynamic replanning to coordinate assets and
optimize their routes, allocation and tasking. It uses an evolutionary algorithm to address the large parametric space of
route-finding problems which is sometimes discontinuous in the ISR domain because of conflicting objectives such as
minimizing asset utilization yet maximizing ISR coverage. EAs are uniquely suited for generating solutions in dynamic
environments and also allow user feedback. They are therefore ideal for "streaming optimization" and dynamic
replanning of ISR mission plans. I2PARS uses the Non-dominated Sorting Genetic Algorithm (NSGA-II) to
automatically generate a diverse set of high performing collection plans given multiple objectives, constraints, and
assets. Intended end users of I2PARS include ISR planners in the Combined Air Operations Centers and Joint
Intelligence Centers. Here we show the feasibility of applying the NSGA-II algorithm and EAs in general to the ISR
planning domain. Unique genetic representations and operators for optimization within the ISR domain are presented
along with multi-objective optimization criteria for ISR planning. Promising results of the I2PARS architecture design,
early software prototype, and limited domain testing of the new algorithm are discussed. We also present plans for future
research and development, as well as technology transition goals.
Recent military operations have demonstrated the use by adversaries of non-traditional or asymmetric military tactics to offset US military might. Rogue nations with links to trans-national terrorists have created a highly unpredictable and potential dangerous environment for US military operations. Several characteristics of these threats include extremism in beliefs, global in nature, non-state oriented, and highly networked and adaptive, thus making these adversaries less vulnerable to conventional military approaches. Additionally, US forces must also contend with more traditional state-based threats that are further evolving their military fighting strategies and capabilities. What are needed are solutions to assist our forces in the prosecution of operations against these diverse threat types and their atypical strategies and tactics. To address this issue, we present a system that allows for the adaptation of a multi-resolution adversarial model. The developed model can then be used to support both training and simulation based acquisition requirements to effectively respond to such an adversary. The described system produces a combined adversarial model by merging behavior modeling at the individual level with aspects at the group and organizational level via network analysis. Adaptation of this adversarial model is performed by means of an evolutionary algorithm to build a suitable model for the chosen adversary.
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