Trade Study: A Two- Versus Three-Soldier Crew for the Mounted Combat System (MCS) and Other Future Combat System Platforms

Mitchell, Diane Kuhl; Samms, Charneta; Henthorn, Thomas J.; Wojciechowski, Josephine Q.

doi:10.21236/ada417992

Cited by 27 publications

(14 citation statements)

References 1 publication

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“…Hierarchical task analysis is an action-oriented approach 5 , while GOMS (Goals, Operators, Methods, Selection rules) 6 is a cognitive approach. Task models and tools such as IMPRINT, may be seen as logistics models coordinating the resources and tasks, representing the individuals as nodes within the network of an organization 9,10 . There are several cognitive architectures that examine detailed cognitive mechanisms.…”

Section: A Human Factors Task Modelsmentioning

confidence: 99%

Capturing Safety Requirements to Enable Effective Task Allocation between Humans and Automaton in Increasingly Autonomous Systems

Neogi

2016

16th AIAA Aviation Technology, Integration, and Operations Conference

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There is a current drive towards enabling the deployment of increasingly autonomous systems in the National Airspace System (NAS). However, shifting the traditional roles and responsibilities between humans and automation for safety critical tasks must be managed carefully, otherwise the current emergent safety properties of the NAS may be disrupted. In this paper, a verification activity to assess the emergent safety properties of a clearly defined, safety critical, operational scenario that possesses tasks that can be fluidly allocated between human and automated agents is conducted. Task allocation role sets were proposed for a human-automation team performing a contingency maneuver in a reduced crew context. A safety critical contingency procedure (engine out on takeoff) was modeled in the Soar cognitive architecture, then translated into the Hybrid Input Output formalism. Verification activities were then performed to determine whether or not the safety properties held over the increasingly autonomous system. The verification activities lead to the development of several key insights regarding the implicit assumptions on agent capability. It subsequently illustrated the usefulness of task annotations associated with specialized requirements (e.g., communication, timing etc.), and demonstrated the feasibility of this approach.

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Section: A Human Factors Task Modelsmentioning

confidence: 99%

Capturing Safety Requirements to Enable Effective Task Allocation between Humans and Automaton in Increasingly Autonomous Systems

Neogi

2016

16th AIAA Aviation Technology, Integration, and Operations Conference

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“…The data aided in developing and testing flexible, adaptive workload-management strategies for the modeled activity. Mitchell et al, (2003) used a task network model to compare and predict workload for twoversus three-soldier crews. They varied the distribution of functions among crew members, and the types of scenarios crews will encounter.…”

Section: Scope Of Hpm Applicationsmentioning

confidence: 99%

“…IMPRINT and its predecessor, Hardware vs. Manpower (HARDMAN), were applied to a wide variety of design projects including analyzing the maintenance staffing needs in Army systems 3 , and the staffing requirements for military vehicles (Mitchell et al, 2003). 4 I discuss the general scope of HPM applications in the next section.…”

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confidence: 99%

Applying Human-performance Models to Designing and Evaluating Nuclear Power Plants: Review Guidance and Technical Basis

O’Hara¹

2009

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“…However, these factors relate to and affect outcomes, such as usability and system performance. Manning is a key factor in many human‐related system trade studies, which often employ IMPRINT [Allender, ; Mitchell et al., ; Mitchell, Samms, and Wojcik, ; Mitchell D. K., ]. The U.S. Navy‐developed predecessor to IMPRINT was created specifically to analyze trades between hardware and manpower [Dickason, Sargent, and Bagnall, ].…”

Section: Introductionmentioning

confidence: 99%

Informing System Design Using Human Performance Modeling

Watson

Rusnock

Miller

et al. 2017

Systems Engineering

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Humans play a key role in the operation and support of most systems and model-based systems engineering (MBSE) offers new opportunities to properly consider human capabilities and involvement. This research presents an approach for systems engineers to integrate system models with human performance modeling for early and more effective system design. Unlike analyses using traditional physics-based models found in most extant MBSE literature, adjusting system parameters for human-based analyses can greatly impact the design of the system itself. Adjusting a human-system parameter can lead to design implications including adjustments to task allocation, process and workflow, and interface design. To demonstrate this, a quantitative case-study approach is used. Starting with a set of Systems Modeling Language (SysML) diagrams, a task analysis is performed to inform an "as is" model of human performance in the Improved Performance Research Integration Tool (IMPRINT). An alternative IMPRINT model is created with varying design parameters and utilized to perform a trade study. Through the analysis, constraints and assumptions placed on the human are verified and the results of varying automation estimated. With current design emphasis in MBSE and model-based engineering (MBE), there is great opportunity to emphasize human considerations and integrate human performance analysis. Published 2017.

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Trade Study: A Two- Versus Three-Soldier Crew for the Mounted Combat System (MCS) and Other Future Combat System Platforms

Cited by 27 publications

References 1 publication

Capturing Safety Requirements to Enable Effective Task Allocation between Humans and Automaton in Increasingly Autonomous Systems

Capturing Safety Requirements to Enable Effective Task Allocation between Humans and Automaton in Increasingly Autonomous Systems

Applying Human-performance Models to Designing and Evaluating Nuclear Power Plants: Review Guidance and Technical Basis

Informing System Design Using Human Performance Modeling

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