Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

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.

This report provides documentation for the completion of the Sandia portion of the ASC Level II Visualization on the platform milestone. This ASC Level II milestone is a joint milestone between Sandia National Laboratories and Los Alamos National Laboratories. This milestone contains functionality required for performing visualization directly on a supercomputing platform, which is necessary for peta-scale visualization. Sandia's contribution concerns in-situ visualization, running a visualization in tandem with a solver. Visualization and analysis of petascale data is limited by several factors which must be addressed as ACES delivers the Cielo platform. Two primary difficulties are: (1) Performance of interactive rendering, which is most computationally intensive portion of the visualization process. For terascale platforms, commodity clusters with graphics processors(GPUs) have been used for interactive rendering. For petascale platforms, visualization and rendering may be able to run efficiently on the supercomputer platform itself. (2) I/O bandwidth, which limits how much information can be written to disk. If we simply analyze the sparse information that is saved to disk we miss the opportunity to analyze the rich information produced every timestep by the simulation. For the first issue, we are pursuing in-situ analysis, in which simulations are coupled directly with analysis libraries at runtime. This milestone will evaluate the visualization and rendering performance of current and next generation supercomputers in contrast to GPU-based visualization clusters, and evaluate the performance of common analysis libraries coupled with the simulation that analyze and write data to disk during a running simulation. This milestone will explore, evaluate and advance the maturity level of these technologies and their applicability to problems of interest to the ASC program. Scientific simulation on parallel supercomputers is traditionally performed in four sequential steps: meshing, partitioning, solver, and visualization. Not all of these components are necessarily run on the supercomputer. In particular, the meshing and visualization typically happen on smaller but more interactive computing resources. However, the previous decade has seen a growth in both the need and ability to perform scalable parallel analysis, and this gives motivation for coupling the solver and visualization.

Abstract not provided.

The present paper is the second in a series published at I/ITSEC that seeks to explain the efficacy of multi-role experiential learning employed to create engaging game-based training methods transitioned to the U.S. Army, U.S. Army Special Forces, Civil Affairs, and Psychological Operations teams. The first publication (I/ITSEC 2009) summarized findings from a quantitative study that investigated experiential learning in the multi-player, PC-based game module transitioned to PEO-STRI, DARWARS Ambush! NK (non-kinetic). The 2009 publication reported that participants of multi-role (Player and Reflective Observer/Evaluator) game-based training reported statistically significant learning and engagement. Additionally when the means of the two groups (Player and Reflective Observer/Evaluator) were compared, they were not statistically significantly different from each other. That is to say that both playing as well as observing/evaluating were engaging learning modalities. The Observer/Evaluator role was designed to provide an opportunity for real-time reflection and meta-cognitive learning during game play. Results indicated that this role was an engaging way to learn about communication, that participants learned something about cultural awareness, and that the skills they learned were helpful in problem solving and decision-making.

The present paper seeks to continue to understand what and how users of non-kinetic game-based missions learn by revisiting the 2009 quantitative study with further investigation such as stochastic player performance analysis using latent semantic analyses and graph visualizations. The results are applicable to First-Person game-based learning systems designed to enhance trainee intercultural communication, interpersonal skills, and adaptive thinking. In the full paper, we discuss results obtained from data collected from 78 research participants of diverse backgrounds who trained by engaging in tasks directly, as well as observing and evaluating peer performance in real-time. The goal is two-fold. One is to quantify and visualize detailed player performance data coming from game play transcription to give further understanding to the results in the 2009 I/ITSEC paper. The second is to develop a set of technologies from this quantification and visualization approach into a generalized application tool to be used to aid in future games’ development of player/learner models and game adaptation algorithms.

Specifically, this paper addresses questions such as, “Are there significant differences in one's experience when an experiential learning task is observed first, and then performed by the same individual?” “Are there significant differences among groups participating in different roles in non-kinetic engagement training, especially when one role requires more active participation that the other?” “What is the impact of behavior modeling on learning in games?” In answering these questions the present paper reinforces the 2009 empirical study conclusion that contrary to current trends in military game development, experiential learning is enhanced by innovative training approaches designed to facilitate trainee mastery of reflective observation and abstract conceptualization as much as performance-based skills.

Abstract not provided.

Abstract not provided.

The 9/30/2009 ASC Level 2 Scalable Analysis Tools for Sensitivity Analysis and UQ (Milestone 3160) contains feature recognition capability required by the user community for certain verification and validation tasks focused around sensitivity analysis and uncertainty quantification (UQ). These feature recognition capabilities include crater detection, characterization, and analysis from CTH simulation data; the ability to call fragment and crater identification code from within a CTH simulation; and the ability to output fragments in a geometric format that includes data values over the fragments. The feature recognition capabilities were tested extensively on sample and actual simulations. In addition, a number of stretch criteria were met including the ability to visualize CTH tracer particles and the ability to visualize output from within an S3D simulation.

Abstract not provided.

Abstract not provided.

This report presents progress on identifying and classifying features involving combustion in turbulent flow using principal component analysis (PCA) and k-means clustering using an in situ analysis framework. We describe a process for extracting temporally- and spatially-varying information from the simulation, classifying the information, and then applying the classification algorithm to either other portions of the simulation not used for training the classifier or further simulations. Because the regions classified as being of interest take up a small portion of the overall simulation domain, it will consume fewer resources to perform further analysis or save these regions at a higher fidelity than previously possible. The implementation of this process is partially complete and results obtained from PCA of test data is presented that indicates the process may have merit: the basis vectors that PCA provides are significantly different in regions where combustion is occurring and even when all 21 species of a lifted flame simulation are correlated the computational cost of PCA is minimal. What remains to be determined is whether k-means (or other) clustering techniques will be able to identify combined combustion and flow features with an accuracy that makes further characterization of these regions feasible and meaningful.

Abstract not provided.