Improved Crowd Control Utilizing a Distributed Genetic

AlgorithmJohn Chaloupek

December 3rd, 2003

Overview

Why Crowd Control?“Distributed” Genetic Algorithm?GoalsDistributed DesignGA Design & RepresentationResultsFuture Work

Crowds

Bad Stuff happens– Fires– Terrorist Attacks– Weapons of Mass Destruction– Natural Disasters

Crowds

People act irrationally in a disaster.– Panic– Confusion

Crowds often make the situation worse.Sometimes the crowd is more dangerous

than the disaster.

Crowd Control

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

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.

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.

“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.

Similar Distributed Projects

Distributed.Net– Cryptography, Optimal Golomb Rulers

Seti@home– Signal Analysis

United Devices– Protein Modeling

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.

Client/Server Model

Server runs GA and passes out members of the population to be evaluated.

Clients evaluate fitness.

Server

GA Design Highlights

Rank based selectionRank based competition (w/Elitist)Uniform crossoverUser specifiable parameters

– Pc, Pm, Steepness of – Pop Size, #of Gens to run, How often to log,

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

Representation - Map

Walls, Exits and Damage sources (fires, chemical spills, etc.) are loaded from a BMP file.

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

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.

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.

Results

Results

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

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.

Questions?