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EXIT = Way Out

EXIT = Way Out. Julian Dymacek April 29. Escape Panic Paper. Dr. Dirk Helbing, Illes J. Farkas, Dr. Tamas Vicsek Point mass simulation Uses psychological forces to keep agents apart and away from walls Uses friction to simulate the clogs in front of doors

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EXIT = Way Out

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  1. EXIT = Way Out Julian Dymacek April 29

  2. Escape Panic Paper • Dr. Dirk Helbing, Illes J. Farkas, Dr. Tamas Vicsek • Point mass simulation • Uses psychological forces to keep agents apart and away from walls • Uses friction to simulate the clogs in front of doors • Found a combination of rushing to doors and following neighbors demonstrated escape panic

  3. Craig Reynolds • Craig Reynolds – Boids • Separation, Alignment, Cohesion • Craig Reynolds – Steering Behaviors • Obstacle avoidance • Wandering • Following

  4. Wander Behavior

  5. What do I want to do? • Reproduce the escape panic simulation • Allow agents to be controlled by behaviors not included in the escape panic paper • Find behaviors that help agents quickly exit from a room

  6. Behaviors • Closest • Distance to door/ max distance • Follow your neighbors • Density of surrounding agents (agent area/ circle area) • Go with the flow • Avg speed of agents in radius no return/ max speed • Popularity • Density of agents around door (agent area/ half circle area)

  7. Chromosome for GA • Each behavior is a 5 bit gene • Wander is the default behavior • Another 5 bit gene represents the order of applying behaviors • A final 5 bit gene encodes desired speed • 30 total bits

  8. Tests • Solved for best strategy with a single agent and multiple agents • Varied the percentage of agents who follow neighbors with 95%, 75%, 50%, 25% and 0% • Used two separate distributions of agents • Agents had 20 seconds to escape • Fitness was 1-(time to escape/ 20)

  9. Results • The Good • Found ways besides go to closest • The Bad • Mostly found go to closest • The Ugly • The multi-agent tests could become inflated

  10. The Good

  11. The Bad

  12. The Ugly • Since multi-agents were spread throughout the clump they influenced the other “dumb” agents in ways which enabled them to get out faster • Clumps of evolving agents together form a small pack which can increase exit speed by not getting trapped behind other agents • Usually got out under 6 seconds

  13. Comments and Future • Hard to debug and figure how multi-agents respond • Sheep herding (aren’t we all just sheep) encouraging people exiting stadiums • More complex environments/ distributions • One final example/moral

  14. Questions, Comments, Cries of Joy?

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