Agentenbasierte Verkehrssimulation mit psychologischen Persönlichkeitsprofilen (DE/BMBF/13028X11)
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Perception is one of the most important cognitive capabilities of an entity since it determines how an entity perceives its environment. The presented work focuses on providing cost efficient but realistic perceptual processes for intelligent virtual agents (IVAs) or NPCs with the goal of providing a sound information basis for the entities' decision making processes. In addition, an agent-central perception process should rovide a common interface for developers to retrieve data from the IVAs' environment. The overall process is evaluated by applying it to a scenario demonstrating its benefits. The evaluation indicates, that such a realistically simulated perception process provides a powerful instrument to enhance the (perceived) realism of an IVA's simulated behavior.
Traffic simulations are generally used to forecast traffic behavior or to simulate non-player characters in computer games and virual environments. These systems are usually modeled in such a way that traffic rules are strictly followed. However, rule violations are a common part of real-life traffic and thus should be integrated into such models.
Realism and plausibility of computer controlled entities in entertainment software have been enhanced by adding both static personalities and dynamic emotions. Here a generic model is introduced which allows the transfer of findings from real-life personality studies to a computational model. This information is used for decision making. The introduction of dynamic event-based emotions enables adaptive behavior patterns. The advantages of this new model have been validated with a four-way crossroad in a traffic simulation. Driving agents using the introduced model enhanced by dynamics were compared to agents based on static personality profiles and simple rule-based behavior. It has been shown that adding an adaptive dynamic factor to agents improves perceivable plausibility and realism. It also supports coping with extreme situations in a fair and understandable way.