Swarms and Crowds

1. Swarms and Crowds Kristen Gardner

2. Outline • Swarm Intelligence • Flocking • Basic • Steering • Swarms • Applications • Animals • Vehicles • People

3. Swarm Intelligence • What is it? • Artificial intelligence that is based on collective behavior of decentralized, self organized systems • Examples? • Flocks of birds, schools of fish (Boids) • Ant colony • Bees

4. What is Flocking? • An example of emergent behavior which resembles the motion of a flock of birds, school of fish, or swarm of insects • First simulated on a computer in 1986 by Craig Reynolds’ program Boids • Considered emergent because unexpected behavior may occur when flocks split up and reunite to avoid obstacles

5. How Does Flocking Work? • The behavior of each Boid is individual, but conforms to a few, relatively simple, rules • Separation • Alignment • Cohesion • Other, more complex rules may also be added • Obstacle avoidance • Goal seeking

6. Basic Rules • separation: steer to avoid crowding local flock mates • alignment: steer towards the average heading of local flock mates • cohesion: steer to move toward the average position of local flock mates

7. Field of View • To keep things “realistic,” respond only to the actions of neighboring Boids • Example • Fancy Example

8. Algorithmic Complexity • Basic un-optimized implementation is O(n2) • Each Boid must calculate its distance from each other Boid • Basic optimized implementation is O(kn) • Area the flock inhabits is divided into a grid with each Boid in a “bin” • Each Boid only needs to check its position relative to Boids in its (or neighboring) bins

9. Using Steering • Model mass (scalar) position (vector) velocity (vector) max_force (scalar) max_speed (scalar) orientation (N basis vectors) • Euler Integration for animation steering_force = truncate (steering_direction, max_force) acceleration = steering_force / mass velocity = truncate (velocity + acceleration, max_speed) position = position + velocity

10. Steering Behaviors • Seek and Flee • Pursue and Evade • Wander • Arrival • Wall Following • Path Following • Flow Field Following

11. Seek and Flee • Go toward or away from a static target • Seek: desired_velocity = normalize (position - target) * max_speed steering = desired_velocity – velocity • Flee: • Inverse of seek

12. Pursuit and Evasion • Similar to Seek and Flee, but estimates the quarry’s position in the future • Based on the distance to the quarry, estimate the number of time steps (the predictor interval T) until the Boid could reach the quarry • Base T on the distance between objects: T=Dc • Based on the quarry’s current velocity, position, and the calculated predictor interval, estimate the target • Use seek or flee algorithm to determine steering

13. Wander • Multiple ways • Random steering force at each frame • Easy to implement • May cause “twitching” behavior • Retain steering direction, but make small displacement • Constrain steering to a sphere (or circle in 2D) in front of the Boid • Size of sphere determines how quickly heading can change

14. Arrival • Similar to seek, but slows when approaching target target_offset = target - position distance = length (target_offset) ramped_speed = max_speed * (distance / slowing_distance) clipped_speed = minimum (ramped_speed, max_speed) desired_velocity = (clipped_speed / distance) * target_offset steering = desired_velocity - velocity

15. Path Following • Boid steers to follow a predetermined path • Velocity-based prediction of future position • Future position projected to nearest point on path • If distance from path is more than path radius, steer toward path

16. Variation of Path Finding • Wall Following • Approach a wall and maintain a certain distance from it • Generate a new goal position and steer toward it • Containment • Use future position to determine if Boid will remain inside area • If not steer back toward the center of area

17. Flow Field Following • Steer to align with flow vectors steering = flow_direction-velocity

18. Obstacle Avoidance • Multiple approaches can be used • Force Field model • Steer-to-avoid

19. Types of Collisions • Obstacles • Stationary • Incoming • Outgoing • Sideswiping • Response • Circumvention • Queuing

20. Force Fields • The equation: • ki is a scaling constant • di is the distance from the agent to the obstacle • ei controls the magnitude of acceleration based on distance • ki and ei should be negative for avoidance, positive values can be used for attraction • Advantages: • Easy to model • Disadvantages: • Field is exactly opposite, so no turning away from straight on • Too weak far away, so long term planning may be required

21. Steer-to-Avoid • Algorithm: • Calculates silhouette for each obstacle • Tests collision with current heading • If there will be a collision, the Boid will aim for a point one body length beyond the silhouette • Advantages: • Only affected by obstacles directly in front of it

22. Combined Behaviors • Crowd Path Following • path following and separation • Leader Following • Leader: wandering, seek random position • Followers: arrival, separation, follow path set by leader • Unaligned Collision Avoidance • Containment, predictive obstacle avoidance • Queuing • Seek, avoid, separation,

23. And the result is… • So, little circles running around are great, but really, what can flocking do? • The Lion King

24. Simple Swarms • An alternative to flocking • Due to complexity, flocking calculations may be prohibitive in real-time applications • Does not attempt to avoid collisions between swarm members • Use with large number of small creatures • Spiders, rats, etc.

25. Swarms • Introducing the swarm • Drop from the ceiling, “explode” from a container • Each agent has a separate timer, set to a random value, so the agents enter the scene gradually • Algorithm • Small changes in movement and direction should keep the agents separate • “Outer zone” • Increment agent’s speed if it is less than the maximum (should vary from agent to agent) • If the agent’s heading is not roughly in the right direction (within some tolerance), rotate back to the correct heading • “Inner zone” • Behavior varies, depending on the overall desired movement of the group

26. Modified swarm • Can add simple collision tests, simple path following, and some other behaviors while still maintaining O(n) complexity • Also can be modified for agents who fly or walk on walls

27. Applications • Why do we care about any of this? • Animals: flocks, schools, herds • People: crowds • Vehicles: traffic • When to flock, when to swarm? • Flocking • Small to medium number of creatures • Use when behavior of creatures is major focus of game play • Pikmin • Swarms • Large groups (tens or hundreds of agents)

28. Animals • Ambient • Enhance the environment • Give more life-like feel • Not relevant to game play • Secondary • Limited amount of interaction with the player • Part of game play, but not the focus • Should behave intelligently, but also limited so as not to distract from goals • Primary • Are focus of the game and have direct relation to primary objectives

29. Understand Behavior - Groups • Flocks • Stay together • Don’t crowd each other • Follow the leader • Switch leaders occasionally • Individuals break away sometimes, but don’t go too far • Schools • Very similar, but tend to change directions more often • High cohesion and alignment, low separation

30. Understand Behavior – Solitary • Does not mean antisocial, just not governed by the behavior of others • Examples – chipmunks, eagles, etc. • Stay in a limited area • Efforts mainly directed toward feeding • May or may not be comfortable with human presence • Maintain “safe” distance, but only depart if pursued • Low alignment and low to moderate cohesion

31. Ambient animals • Bugs - Example behavior for butterflies • Don’t fly in straight line • Don’t always flap their wings • Don’t flap wings in full beat • Attracted to flowers and bright colors • Avoid moving objects • Stay in limited area • Easily governed by simple rules

32. Primary and Secondary • Tend to follow a schedule throughout the day • Frequent a fixed number of places • Have more than one active sensing mechanism, some more keen than others • Senses are affected by factors in the environment • Avoid human contact, but may be dangerous when cornered • Typically predictable, but should also demonstrate unexpected behavior occasionally • Require more complex individual behavior than ambient animals

33. Traffic • Must obey traffic rules • Stop signs and signals, speed limits, drive on correct side of street • Can be implemented with messages when the vehicle approaches specific objects • Set precedence • A stop sign or pedestrian is more important than maintaining a certain speed • Intersections – ticket system • In order to go through, must be in possession of ticket • After passing through, vehicle relinquishes control of the ticket

34. People • “Milling” • Low max speed, planning for collision avoidance, small changes in heading, follow social/safety conventions (stay on sidewalk) • Pedestrians on sidewalk, small groups standing around • Organized behavior • Pay more attention to other agents • Queuing, military formations, “doing the wave,” waiting at cross-walks, etc. • Traumatic behavior • High maximum speed, large changes in heading, ignore conventions (running into traffic) • Run away from the crazy person with a big gun

35. Rome: Total War • Strategy game – both turn-based and real time • Game engine • Has the ability to render 30,000 units on a single battle field • Used by Decisive Battles (History Channel) to recreate historical battles • Battle of Cannae • Rome: Total War

36. Summary • Flocking • Emergent behavior that imitates a flock, school, or herd • Computationally expensive for large groups • Swarms • Use when flocking too expensive (hundreds of agents) • Organic motion, but flock mates may collide • Applications • Video games, movies/TV, simulations • Animals, vehicles, people • Understand what you want to model before you model it

37. Thank you! Questions or Comments?