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Improved Crowd Control Utilizing a Distributed Genetic Algorithm

Improved Crowd Control Utilizing a Distributed Genetic Algorithm. John Chaloupek December 3 rd , 2003. Overview. Why Crowd Control? “Distributed” Genetic Algorithm? Goals Distributed Design GA Design & Representation Results Future Work. Crowds. Bad Stuff happens Fires

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Improved Crowd Control Utilizing a Distributed Genetic Algorithm

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

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