A history of United States military simulation

Raymond, R Hill; Miller, Jude

doi:10.1109/wsc.2017.8247799

Cited by 16 publications

(5 citation statements)

References 22 publications

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“…In this case, the assumptions largely revolve around the type of engagement that can be simulated. Despite the myriad of tactical aspects in a military engagement, here we will describe three broad categories: (1) determining an outcome, (2) the specific scenario and (3) data granularity.…”

Section: Assumptions Inherent To An Evaluation Of Warfighter Performancementioning

confidence: 99%

“…Variance itself then becomes a factor represented in simulations. The application is also not novel to small arms combat as Monte Carlo simulations were first applied to Vietnam War-era modeling in describing small unit activities (Adams et al, 1961;Bonder, 2002;De Laquil, 1980;Monahan and DuBois, 1979) [1]. However, small arms combat applications typically involved special operations forces actions such as theft of nuclear material.…”

Section: Introductionmentioning

confidence: 99%

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A primer on using Monte Carlo simulations to evaluate marksmanship

Biggs,

Hamilton

2023

JDAL

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PurposeEvaluating warfighter lethality is a critical aspect of military performance. Raw metrics such as marksmanship speed and accuracy can provide some insight, yet interpreting subtle differences can be challenging. For example, is a speed difference of 300 milliseconds more important than a 10% accuracy difference on the same drill? Marksmanship evaluations must have objective methods to differentiate between critical factors while maintaining a holistic view of human performance.Design/methodology/approachMonte Carlo simulations are one method to circumvent speed/accuracy trade-offs within marksmanship evaluations. They can accommodate both speed and accuracy implications simultaneously without needing to hold one constant for the sake of the other. Moreover, Monte Carlo simulations can incorporate variability as a key element of performance. This approach thus allows analysts to determine consistency of performance expectations when projecting future outcomes.FindingsThe review divides outcomes into both theoretical overview and practical implication sections. Each aspect of the Monte Carlo simulation can be addressed separately, reviewed and then incorporated as a potential component of small arms combat modeling. This application allows for new human performance practitioners to more quickly adopt the method for different applications.Originality/valuePerformance implications are often presented as inferential statistics. By using the Monte Carlo simulations, practitioners can present outcomes in terms of lethality. This method should help convey the impact of any marksmanship evaluation to senior leadership better than current inferential statistics, such as effect size measures.

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Section: Assumptions Inherent To An Evaluation Of Warfighter Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A primer on using Monte Carlo simulations to evaluate marksmanship

Biggs,

Hamilton

2023

JDAL

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“…SISO grew out of the need of military simulation to interoperate to enable group of soldiers to “train as they fight.” As captured in various chapters and conference papers, 7–9 the idea for interconnected training simulators took shape with the Defense Advanced Research Project Agency (DARPA) project SIMNET. Out of this prototype grew the IEEE 1278 Standard for Distributed Interactive Simulation (DIS).…”

Section: Selected Domain-specific Interoperability Effortsmentioning

confidence: 99%

Conceptual alignment for simulation interoperability: lessons learned from 30 years of interoperability research

Tolk

2023

SIMULATION

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This position paper provides first, a literature survey of selected domains with significant contributions to simulation interoperability research, such as defense and healthcare simulation, including insights from academia and research organizations. Next, four main interoperability methods are reviewed, i.e., messages-oriented methods, use of information exchange models and common object models, and reference modeling, including ontological means. This review leads to the observation that successful approaches do not only rely on technical solutions but are also using conceptual alignment of the underlying models as well. The Levels of Conceptual Interoperability Model is revisited to consider how it contributed—and still contributes today—to reach meaningful interoperability between model-based solutions. Its descriptive and prescriptive use is discussed, and it is applied to the four methods to show its practical applicability.

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“…The M&S ecosystem addresses the realization of the endto-end process conceptualized in Figure 2, this process contains autonomy and OODA-loop related concepts as discussed in Figure 1. The process shown in Figure 2 creates an M&S environment, including key components of analytics models (abstract but convenient) to physical testing (realistic but costly) (Hill and Miller 2017), for testing of autonomy enabling technologies and methods. The Digital M&S process in Figure 2 is critical for rapidly testing ideas because physical platforms are complex and require consideration of real world nuances.…”

Section: Research Motivationsmentioning

confidence: 99%

From Lab to Internship and Back Again: Learning Autonomous Systems through Creating a Research and Development Ecosystem

Bihl

Jenkins

Cox

et al. 2019

AAAI

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As research and development (R&D) in autonomous systems progresses further, more interdisciplinary knowledge is needed from domains as diverse as artificial intelligence (AI), bi-ology, psychology, modeling and simulation (M&S), and robotics. Such R&D efforts are necessarily interdisciplinary in nature and require technical as well as further soft skills of teamwork, communication and integration. In this paper, we introduce a 14 week, summer long internship for developing these skills in undergraduate science and engineering interns through R&D. The internship was designed to be modular and divided into three parts: training, innovation, and application/integration. The end result of the internship was 1) the development of an M&S ecosystem for autonomy concepts, 2) development and robotics testing of reasoning methods through both Bayesian methods and cognitive models of the basal ganglia, and 3) a process for future internships within the modular construct. Through collaboration with full-time professional staff, who actively learned with the interns, this internship incorporates a feedback loop to educate and per-form fundamental R&D. Future iterations of this internship can leverage the M&S ecosystem and adapt the modular internship framework to focus on different innovations, learning paradigms, and/or applications.

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A history of United States military simulation

Cited by 16 publications

References 22 publications

A primer on using Monte Carlo simulations to evaluate marksmanship

A primer on using Monte Carlo simulations to evaluate marksmanship

Conceptual alignment for simulation interoperability: lessons learned from 30 years of interoperability research

From Lab to Internship and Back Again: Learning Autonomous Systems through Creating a Research and Development Ecosystem

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