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Bart van Greevenbroek

Bart van Greevenbroek. Controlling the movement of crowds in computer graphics by using the mechanism of particle swarm optimization. Overview. Authors The Paper Particle Swarm Optimization Algorithm used with PSO Experiment Assessment conclusion. Authors.

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Bart van Greevenbroek

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  1. Bart van Greevenbroek Controlling the movement of crowds in computer graphics by usingthe mechanism of particle swarm optimization

  2. Overview • Authors • The Paper • Particle Swarm Optimization • Algorithm used with PSO • Experiment • Assessment • conclusion

  3. Authors Ying-ping Chen Ying-yin Lin

  4. The paper • Published in 2009

  5. Particle Swarm Optimisation • Developed by Kennedy and Eberhart • Published in 1995 • Inspired by flocking of birds and schools of fish • Solution is modeled as a flying particle in a hyper-plane

  6. Particle Swarm Optimisation (2) = velocity of particle i at the next timestep = the weight for the previous velocity = the best position where this particle had been = the overall global best position ever achieved by the swarm = cognitive and social parameters, deciding the influence of Pblsand Pbgs = random factor, to produce varied paths. = position of particle iat the current timestep.

  7. Particle Swarm Optimisation (3)

  8. Particle Swarm Optimisation (4) • Every particle has an objective function, which can influence a and . • It does not take obstacles into account, making PSO incompatible for crowd simulation in its current form.

  9. Algorithm with PSO • Each pedestrian is considered a particle in 2d space, with position pi = [pix , piz] T a direction Di = [Dix ,Diz ]T and a speed S.

  10. Direction function and are unit vectors.

  11. Position function The new position is determined by the direction and the speed.

  12. Method • Speed is updated to the inverse of the objective function. This varies the pace of each person. • If a particle approaches an obstacle, the speed will be slower due to greater objective values.

  13. Exponential cost function

  14. Exponential Obstacle Model

  15. Objective Cost Function = balancing factor that decides the balance between avoiding obstacles and reaching the goal. = low if the cost to the goal is high. = the object that has the highest cost (closest obstacle)

  16. Probability is a constant factor that can influence the probability of the new position being accepted.

  17. Probability (2)

  18. Experiments • A number of experiments were performed • To show how bad this method is.

  19. Experiments • No Details on the implementation are given • No system specs • No performance • No way to compare with other methods • Except the movies which show very non-human like behavior

  20. Engrish

  21. What can we learn from this? • Swarm Intelligence is NOT a good way to model human behavior • Other predictive methods look much nicer. • The desire to make something general will not work when you have specific situations requiring specific solutions.

  22. Occilations • In the abstract the authors state that they want to avoid oscillations which works with the original PSO. But the examples shown oscillate like ants

  23. What were they thinking?

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