The Marine Corps' Project Albert seeks to model complex phenomenon by observing the behavior of relatively simple simulations over thousands of runs. A rich data base is developed by running the simulations thousands of times, varying the agent and scenario input parameters as well as the random seeds. Exploring this result space may provide significant insight into nonlinear, surprising, and emergent behaviors. Capturing these results can provide a path for making the results usable for decision support to a military commander. This paper presents two data mining approaches, rule discovery and Bayesian networks, for analyzing the Albert simulation data. The first approach generates rules from the data and then uses them to create descriptive model. The second generates Bayesian Networks which provide a quantitative belief model for decision support. Both of these approaches as well as the Project Albert simulations are framed in the context of a system architecture for decision support.

The Marine Corps' Project Albert seeks to model complex phenomenon by observing the behavior of relatively simple simulations over thousands of runs. These simulations are based upon lightweight agents, whose essential behavior has been distilled down to a small number of rules. By varying the parameters of these rules, Project Albert simulations can explore emergent complex nonlinear behaviors with the aim of developing insight not readily provided by first principle mathematical models. Thousands of runs of Albert simulation models create large amount of data that describe the association/correlation between the simulation input and output parameters. Understanding the associations between the simulation input and output parameters is critical to understanding the simulated complex phenomenon. This paper presents a data mining approach to analyzing the large scale and highly uncertain Albert simulation data. Specifically, a characteristic rule discovery algorithm is described in the paper together with its application to the Albert simulation runtime data.