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

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0147-13-C-7414
Agency Tracking Number: B12B-006-0006
Amount: $99,937.00
Phase: Phase I
Program: STTR
Awards Year: 2013
Solicitation Year: 2012
Solicitation Topic Code: MDA12-T006
Solicitation Number: 2012.B
Small Business Information
100 E. Rivercenter Blvd, Suite 100, Covington, KY, -
DUNS: 128933996
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Stu Rodgers
 Managing Director
 (937) 903-0558
 s.rodgers@tier1performance.com
Business Contact
 Kevin Moore
Title: Chief Learning Officer
Phone: (859) 663-2114
Email: 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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