A Distributed Intelligent Agent Architecture for Simulating Aggregate-Level Behavior and Interactions on the Battlefield

Combat simulations are playing an increasing role in training in the military. This is especially true for training staff officers in tactical operations centers (TOCs) at intermediate echelons. In the University XXI project at Texas A&M University, we are developing a digital battle-staff trainer (DBST) system. For implementing the DBST, a multi-agent architecture called TaskableAgents has been designed for simulating the internal operations, decision-making, and interactions of battalion TOC staffs. The core of the TaskableAgents Architecture is a knowledge representation language called TRL. The application of knowledge in TRL to select actions for an agent is carried out by an algorithm called APTE. By communicating and sharing information based on reasoning about each other’s roles, the TaskableAgents Architecture allows multiple agents to work together as teams to accomplish collective goals. This general approach could be used to simulate the intelligent behaviors of a wide variety of tactical decision-making units in military combat simulations.