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 Found a combination of rushing to doors and following neighbors demonstrated escape panic

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

Wander Behavior

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

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)

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

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)

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

The Good SpeedClosestDensityFlowFriendsOrderFitness 100% S (2431) % C (2413) % C (2413) % S (2413)

The Bad SpeedClosestDensityFlowFriendsOrderFitness 50% S (4231) % C (2431) % C (1234) % S (4132)

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

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

Questions, Comments, Cries of Joy?