Modeling and simulation of military operations are becoming popular with the widespread application of artificial intelligence methods. As the decision makers would like to analyze the results of the simulations in greater details, entity-level simulation of physical world and activities of actors (soldiers, tanks, etc) is unavoidable. In this thesis, a multiagent framework for simulating task driven autonomous activities of actors or group of actors is proposed. The framework is based on BDI-architecture where an agent is composed of beliefs, goals and plans. Besides, an agent team is organized hierarchically and decisions at different levels of the hierarchy are governed by virtual command agents with their own beliefs, goals and plans. The framework supports an interpreter that realizes execution of single or multiagent plans coherently. The framework is implemented and a case study demonstrating the capabilities of the framework is carried out.

In large scale entity-level military force-on-force simulations it is essential to know when one entity can visibly see another entity. This visibility determination plays an important role in the simulation and can affect the outcome of the simulation. When virtual Computer Generated Forces (CGF) are introduced into the simulation these intervisibilities must now be calculated by the virtual entities on the battlefield. But as the simulation size increases so does the complexity of calculating visibility between entities. This thesis presents an algorithm for performing these visibility calculations using Graphical Processing Units (GPU) instead of the Central Processing Units (CPU) that have been traditionally used in CGF simulations. This algorithm can be distributed across multiple GPUs in a cluster and its scalability exceeds that of CGF-based algorithms. The poor correlations of the two visibility algorithms are demonstrated showing that the GPU algorithm provides a necessary condition for a "Fair Fight" when paired with visual simulations.