A Computational Model of Situation Awareness for MOUT Simulations

Situation awareness is the perception of environmental elements within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future. The quality of situation awareness directly affects the decision making process for human soldiers in Military Operations on Urban Terrain (MOUT). It is therefore important to accurately model situation awareness in order to generate realistic tactical behaviors for the non-player characters (also known as bots) in MOUT simulations. This is a very challenging problem due to the time constraints in decision-making process and the heterogeneous cue types involved in MOUT. Although there are some theoretical models on situation awareness, they generally do not provide computational mechanisms suitable for MOUT simulations. In this paper, we propose a computational model of situation awareness for the bots in MOUT simulations. The computational model aims to form up situation awareness quickly with some key cues of the tactical situation. It is also designed to work together with some novel features that help to produce realistic tactical behaviors. These features include case-based reasoning, qualitative spatial representation and expectations. The effectiveness of the computational model is assessed with Twilight City, a virtual environment that we have built for MOUT simulations.