Human Performance Modeling Mark-up Language (HMP-ML)

Award Information
Agency:
Department of Defense
Branch
Missile Defense Agency
Amount:
$99,937.00
Award Year:
2013
Program:
STTR
Phase:
Phase I
Contract:
HQ0147-13-C-7414
Agency Tracking Number:
B12B-006-0006
Solicitation Year:
2012
Solicitation Topic Code:
MDA12-T006
Solicitation Number:
2012.B
Small Business Information
Tier 1 Performance Solutions, LLC
100 E. Rivercenter Blvd, Suite 100, Covington, KY, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
128933996
Principal Investigator:
Stu Rodgers
Managing Director
(937) 903-0558
s.rodgers@tier1performance.com
Business Contact:
Kevin Moore
Chief Learning Officer
(859) 663-2114
k.moore@tier1performance.com
Research Institution:
Carnegie Mellon University
Christian Lebiere
Department of Psychology
Baker Hall, 342C
Pittsburgh, PA, 15213-
(412) 268-6028
Nonprofit college or university
Abstract
The modeling and simulation of human performance is often difficult because there is no uniform framework for expressing the content and structure of a human performance model and all but impossible to compare and contrast across different models despite the abundance of quantitative modeling tools. The inability to communicate model structure and content is not just a practical shortcoming; it is a major impediment to assessing the validity, plausibility, and extensibility of human performance models. We see a significant opportunity to advance the state of the art in human performance modeling with the development of a uniform language for expressing the structure and content of a model. The development of this language, the Human Performance Modeling Mark-up Language (HPM-ML), will follow directly from our efforts to develop models of a Human-In-Control operating a Ballistic Missile Defense System. The goal in developing the HPM-ML is not to impose top-down methodological standards across human performance modelers, but rather to provide a common vocabulary to express what is already contained in current models. The work in Phase I will lead to a constructive proof-of-concept solution that will satisfy MDA"s need for more robust operator models in support of simulation-based training and analysis.

* information listed above is at the time of submission.

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