Agent-Based Approaches for Behavioural Modelling in Military Simulations

Behavioral modeling of combat entities in military simulations by creating synthetic agents in order to satisfy various battle scenarios is an important problem. The conventional modeling tools are not always sufficient to handle complex situations requiring adaptation. To deal with this Agent-Based Modeling (ABM) is employed, as the agents exhibit autonomous behavior by adapting and varying their behavior during the course of the simulation whilst achieving the goals. Synthetic agents created by means of Computer Generated Force (CGF) is a relatively recent approach to model behavior of combat entities for a more realistic training and effective military planning. CGFs, are also sometimes referred to as SemiAutomated Forces (SAF) and enables to create high-fidelity simulations. Agents are used to control and augment the behavior of CGF entities, hence converting them into Intelligent CGF (ICGF). The intelligent agents can be modeled to exhibit cognitive abilities. For this review paper, extensive papers on stateof-the-art in agent-based modeling approaches and applications were surveyed. The paper assimilates issues involved in ABM with CGF as an important component of it. It reviews modeling aspects with respect to the interrelationship between ABM and CGF, which is required to carry out behavioral modeling. Important CGFs have been examined and a list with their significant features is given. Another issue that has been reviewed is that how the synthetic agents having different capabilities are implemented at different battle levels. Brief mention of state-of-the-art integrated cognitive architectures and a list of significant cognitive applications based on them with their features is given. At the same time, the maturity of ABM in agent-based applications has also been considered.