A development environment for operational concepts and systems engineering analysis.

The work reported in this document involves a development effort to provide combat commanders and systems engineers with a capability to explore and optimize system concepts that include operational concepts as part of the design effort. An infrastructure and analytic framework has been designed and partially developed that meets a gap in systems engineering design for combat related complex systems. The system consists of three major components: The first component consists of a design environment that permits the combat commander to perform 'what-if' types of analyses in which parts of a course of action (COA) can be automated by generic system constructs. The second component consists of suites of optimization tools designed to integrate into the analytical architecture to explore the massive design space of an integrated design and operational space. These optimization tools have been selected for their utility in requirements development and operational concept development. The third component involves the design of a modeling paradigm for the complex system that takes advantage of functional definitions and the coupled state space representations, generic measures of effectiveness and performance, and a number of modeling constructs to maximize the efficiency of computer simulations. The system architecture has been developed to allow for a future extension in which the operational concept development aspects can be performed in a co-evolutionary process to ensure the most robust designs may be gleaned from the design space(s)

Real-time individualized training vectors for experiential learning.

Military training utilizing serious games or virtual worlds potentially generate data that can be mined to better understand how trainees learn in experiential exercises. Few data mining approaches for deployed military training games exist. Opportunities exist to collect and analyze these data, as well as to construct a full-history learner model. Outcomes discussed in the present document include results from a quasi-experimental research study on military game-based experiential learning, the deployment of an online game for training evidence collection, and results from a proof-of-concept pilot study on the development of individualized training vectors. This Lab Directed Research & Development (LDRD) project leveraged products within projects, such as Titan (Network Grand Challenge), Real-Time Feedback and Evaluation System, (America's Army Adaptive Thinking and Leadership, DARWARS Ambush! NK), and Dynamic Bayesian Networks to investigate whether machine learning capabilities could perform real-time, in-game similarity vectors of learner performance, toward adaptation of content delivery, and quantitative measurement of experiential learning

Real-time individualized training vectors for experiential learning.

Military training utilizing serious games or virtual worlds potentially generate data that can be mined to better understand how trainees learn in experiential exercises. Few data mining approaches for deployed military training games exist. Opportunities exist to collect and analyze these data, as well as to construct a full-history learner model. Outcomes discussed in the present document include results from a quasi-experimental research study on military game-based experiential learning, the deployment of an online game for training evidence collection, and results from a proof-of-concept pilot study on the development of individualized training vectors. This Lab Directed Research & Development (LDRD) project leveraged products within projects, such as Titan (Network Grand Challenge), Real-Time Feedback and Evaluation System, (America's Army Adaptive Thinking and Leadership, DARWARS Ambush! NK), and Dynamic Bayesian Networks to investigate whether machine learning capabilities could perform real-time, in-game similarity vectors of learner performance, toward adaptation of content delivery, and quantitative measurement of experiential learning