1. Improved Crowd Control Utilizing a Distributed Genetic Algorithm John Chaloupek December 3 rd, 2003

2. Overview  Why Crowd Control?  “Distributed” Genetic Algorithm?  Goals  Distributed Design  GA Design & Representation  Results  Future Work

3. Crowds  Bad Stuff happens –Fires –Terrorist Attacks –Weapons of Mass Destruction –Natural Disasters

4. Crowds  People act irrationally in a disaster. –Panic –Confusion  Crowds often make the situation worse.  Sometimes the crowd is more dangerous than the disaster.

5. Crowd Control  First Responders (Police, Fire Dept., etc.) have limited capabilities to deal with crowds. –Barriers –Riot gear

6. Why use an EA?  Doable –Few other ways exist to simulate crowd behavior. –Can test new methods and ideas before putting them to work in a genuine situation.

7. Why use an EA?  Novel Methods –EA’s can help gather support for new methods that have yet to be proven effective.  Unexpected Discoveries –Could come up with methods that haven’t been thought of before.

8. “Distributed” GA?  Actually more of a Client/Server model.  Fitness evaluation is the most computationally intensive part of real world sized problems.  Fitness evaluations can be done in parallel, on multiple processors or multiple machines.

9. Similar Distributed Projects  Distributed.Net –Cryptography, Optimal Golomb Rulers  Seti@home –Signal Analysis  United Devices –Protein Modeling

10. Goals  See if a system for simulating crowd behavior & crowd control using a GA can be developed.  Reduce (virtual) fatalities.  Do it all in a reasonable amount of time.

11. Client/Server Model  Server runs GA and passes out members of the population to be evaluated.  Clients evaluate fitness.

12. Server

13. GA Design Highlights  Rank based selection  Rank based competition (w/Elitist)  Uniform crossover  User specifiable parameters –Pc, Pm, Steepness of –Pop Size, #of Gens to run, How often to log,

14. GA Design Highlights  User specifiable parameters –Pc, Pm –Steepness of the Rank based probabilities. Can set independently for selection and competition. –Pop Size, #of Gens to run –How often to log –Can specify a RNG Seed

15. Representation - Map  Walls, Exits and Damage sources (fires, chemical spills, etc.) are loaded from a BMP file.

16. Representation - Members  Members consist of what actions could be taken to control a crowd. –Place barricades –Set up noise sources –Direct people away from the scene

17. Evaluation  Simplistic AI “victims” are randomly placed on the scene. –Panic –Shortest Route to exit –Run away from most damage/noise –Follow the crowd  Try to pick proportions to most accurately simulate real situation.

18. Fitness  As victims remain on the scene, and fail to get away from sources of damage, they become hurt.  Fitness is the average of the health of the victims.

19. Results

20.  23.6% Improvement in 100 generations. –Pop Size: 1000 –B of Selection: 2 –B of Competition: 2 –Prob. Crossover:.2 –Prob. Mutation:.2

21. Summary  Client/Server code not working all that great.  Lots of room to expand in the future.  Surprisingly good results for what’s currently running.

22. Questions?