SteerPlex works to define a salient set of scenario features that can be used to identify the relative complexity of a steering scenario.
The complexity of interactive virtual worlds has increased dramatically in recent years, with a rise in mature solutions for designing large-scale environments and populating them with hundreds and thousands of autonomous characters. The tremendous surge in the development of crowd simulation techniques has paved the way for a new research direction that aims to analyze and evaluate these algorithms. An interesting problem that arises in this context, and that has received little attention to date, is whether we can predict the complexity of a steering scenario by analyzing the configuration of the environment and the agents involved. This is the problem we address in this paper. First, we statically analyze an input scenario and compute a set of novel salient features which characterize the expected interactions between agents and obstacles during simulation. Using a statistical approach, we automatically derive the relative influence of each feature on the complexity of a scenario in order to derive a single numerical quantity of expected scenario complexity. We validate our proposed metric by proving a strong negative correlation between the statically computed expected complexity and the dynamic performance of three published crowd simulation techniques on a large number of representative scenarios including movingAI benchmarks.
This video demonstrates some of the example results of parameter optimization.