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?