Machine Learning

Levels of “realism”

Many motion models are described as parametric functions. This means that the trajectories they describe are different depending on some input values. Changing the values of the parameters of a pedestrian navigation algorithm affects the trajectory of a virtual human siulated using it. Therefore, the parameter values affect the “realism” of a simulated crowd.

Simulated crowd, very dense

The goal is to simulate groups of virtual agents that behave just like human crowds

Autonomous Learning

Machine Learning consits on creating the tools needed for a machine to autonomously learn how to perform a task or explain an event. In the field of crowd simulation Machine Learning can be used in a crowd simulator to autonomously learn how to simulate human trajectories.

Our research group tries to apply reinforcemnet learning techniques (a subfield of Machine Learning) to autonomously find the best parameter values for a motion model. The learning algorithms can have a variety of goals e.g. minimising the distance to real data, for simulated pedestrians to reaching their goal as fast as possible without colliding, etc.

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