Enhanced Military Decision Modeling Using a MultiAgent System Approach

The U.S. military uses modeling and simulation as a tool to help meet its warfighting needs. A key element within military simulations is the ability to accurately represent human behavior. This is especially true in a simulation's ability to emulate realistic military decisions. However, current decision models fail to provide the variability and flexibility that human decision makers exhibit. Further, most decision models are focused on tactical decisions and ignore the decision process of senior military commanders at the operational level of warfare. In an effort to develop a better decision model that would mimic the decision process of a senior military commander, this research sought to identify an underlying cognitive process and computational techniques that could adequately implement it. Recognition-Primed Decision Making (RPD) was identified as one such model that characterized this process. Multiagent system simulation was identified as a computational system that could mimic the cognitive process identified by RPD. The result was a model of RPD called RPDAgent. Using an operational military decision scenario to test model validity, decisions produced by RPDAgent were compared against decisions made by military officers. It was found that RPDAgent produced decisions that were equivalent to its human counterparts. RPDAgent's decisions were not optimum decisions, but decisions that reflected the variability inherent in those made by humans in an operational military environment.