simulated disasters. Crowd behavior can be observed during panic and non-panic conditions. When natural and unnatural events toss social ideals into a twisting chaotic bind, such as the events of 9/11 and hurricane Katrina, humanity's social capabilities are truly put to the test. Military programs are looking more towards simulated training, involving emergency responses, due to their cost-effective technology as well as how effective the learning can be transferred to the real world. Many events that may start out controlled can have a twisting event that turns them into catastrophic situations, where decisions need to be made on the spot. It is these situations in which crowd dynamical understanding would play a vital role in reducing the potential for anarchy. Modeling techniques of crowds vary from holistic or network approaches to understanding individualistic or behavioral aspects of each agent. For example, the Social Force Model describes a need for individuals to find a balance between social interaction and physical interaction. An approach that incorporates both aspects, and is able to adapt depending on the situation, would better describe natural human behavior, always incorporating some measure of unpredictability. With the use of multi-agent models understanding these complex behaviors has become a much more comprehensible task. With the use of this type of software, systems can now be tested under extreme conditions, and simulate conditions over long periods of time in the matter of seconds. In some situations, the behavior of swarms of non-human animals can be used as an experimental model of crowd behavior. The panic behavior of ants when exposed to a repellent chemical in a confined space with limited exit routes has been found to have both similarities and differences to equivalent human behavior. Modeling individual behaviors Hacohen, Shoval and Shvalb formulated the drivers-pedestrians dynamics at congested conflict spots. In such scenarios, the drivers and/or pedestrians do not closely follow the traffic laws. The model is based on the Probabilistic Navigation_function (PNF), which was originally developed for robotics motion planning. The algorithm constructs a trajectory according to the probability for collision at each point in the entire crossing area. The pedestrian then follow a trajectory that locally minimizes their perceived probability for collision. Helbing proposed a model based on physics using a particle system and socio-psychological forces in order to describe human crowd behavior in panic situation, this is now called the Helbing Model. His work is based on