More Complex Movement AI

1. More Complex Movement AI Flocking and more

2. Movement in Groups • NPC groups can move in cohesive groups not just independently • Meadow of sheep grazing? • Hunting flock of birds? • Ants? Bees? Creatures? • Other types of computer controlled NPCs • Humans, Orcs, Catapults? • Squadrons of aircraft? • Friendly soldier squads? • Simulate crowds of people loitering?

3. Movement in Groups • Coordinated group movement, the idea: • To have the NPCs move with the illusion of having purpose and coordination • One of the earliest, most successful group behavior  Flocking • “Flocks, Herds, and Schools: A Distributed Behavioral Model”, Craig Reynolds, SIGGRAPH 1987 • Originally intended for birds, fish and other creatures, but it can be modified for other types of NPCs

4. Flocking

5. Boids • Term used by Craig Reynolds to refer to this simulated flocks • Leaderless flock – able to stick in a group • 3 simple rules • Cohesion • Alignment • Separation • Neighborhood: Defines the area where these rules will come to effect

6. Cohesion • Have each unit steer towards the average position of its neighbors • Units are attracted to one another as long as they are within range

7. Alignment • Have each unit steer so as to align itself to the average heading of its neighbors. • Match direction of units around it that it can detect

8. Separation • Have each unit steer to avoid hitting its neighbors. • Units are repelled by non-member units or obstacles. Repel effect is inversely prop. to distance from unit

9. Neighborhood • Flocking neighborhood creates a range that units can detect for other same-group units, other-group units

10. Neighborhood • Some implementations use two neighborhoods – one for detection of units, one for separation to avoid other units

11. Neighborhood • Typically, a visibility arc or field-of-view (FOV) is used to define the neighborhood • Is this practical? • To what extend is each unit aware of its neighbors?

12. Unit Visibility • Each unit is aware of its local surroundings • Each unit does not necessarily know what the entire group is doing at any given time

13. Unit Visibility • Visibility arc defined by 2 parameters – arc radius r and angle θ • How do these parameters affect flocking motion?

14. Unit Visibility • Large radius? Small radius? • Wide FOV? Narrow FOV?

15. FOV determines formation • Narrow FOV: Squadron of jets, Sneaking up behavior • Wide FOV: Group of birds, Military army

16. Steering for Flocking • Steering forces to be applied on the units • Treat each unit as a rigid body that is able to turn and apply net steering force accumulated from each flocking rule • 2 important techniques when implementing flocking • Tuning is required so that no single rule dominates • Modulation of steering forces so that contribution is not constant for all units

17. Implementation • In each game loop • Cycle thru all units in the flock to acquire data (direction, speed, etc.) from unit’s neighbors • For each unit, update with net steering force from the three rules • Each unit must update its view of the world each game loop (cycle thru all units in the flock) • Refer to textbook for more details on the implementation code snippet

18. Sample Variable Set void DoUnitAI(inti) { int j; int N; // Number of neighbors Vector Pave; // Average position vector Vector Vave; // Average velocity vector Vector Fs; // Net steering force Vector Pfs; // Point of application of Fs Vector d, u, v, w; double m; // multiplier, +1 or -1 boolInView; boolDoFlock = WideView||LimitedView||NarrowView; intRadiusFactor; . . . }

19. Cohesion - Implementation • Calculate average position – vector sum of their respective positions divided by total number of neighbors • Determine direction to turn and angle to steer towards • Steering force effected = Direction multiplier * Max steering force * angle of steering / scale factor

20. Alignment - Implementation • Normalize each unit’s velocity vector to get heading unit vectors • Calculate average heading of all units – sum of heading unit vectors divided by total number of neighbors • Effected steering force is calculated same way as cohesion

21. Separation - Implementation • Separation is enforced by steering away from any neighbor that is within view AND within prescribed minimum separation distance • Because this steering force is corrective, direction multiplier goes the opposite way • Effected steering force = Direction multiplier * Max steering force * (Unit length * separation factor) / separation distance

22. Try this niec flocking demo • http://www.lalena.com/ai/flock/

23. Obstacle Avoidance • Flocking would be much more realistic if units also avoid running into objects in the game world • To detect whether an obstacle is in the unit’s path ahead, imagine that each unit has “feelers” like those on insects! • Well, if one feeler is not enough, maybe you might need a few feelers? • Let’s see how a single “feeler” works…

24. Obstacle Avoidance • v : “feeler” • Calculate vector a • Project a onto v by dot product to obtain p • Subtract p from a to get vector b • Test conditions: • Magnitude (p) < Magnitude (v) • Magnitude(b) < Radius (r) • If both tests pass, corrective steering required, otherwise unit can continue on its current heading

25. Obstacle Avoidance • Corrective force can be calculated as inversely prop. to distance from unit to the center of obstacle or Magnitude (a) • Effected steering force = Direction multiplier * Max steering force * (Collision Visibility Factor * Unit length for Magnitude(v) / Magnitude(a) )

26. Obstacle Avoidance - Remarks • This obstacle avoidance algo will not necessarily guarantee zero collisions between units and obstacles. What are some likely problems? • What we have seen so far only applies to circular obstacles. What about block (rectangular) obstacles or other free forms shapes?

27. Follow the Leader • So far, flocking behaviors are leaderless • By combining classic flocking with leader-based AI, many new possibilities are available! • Flocks may have greater purpose if follow a leader • Question: How to designate leader? Should we “appoint” a unit as leader? Or should we let them sort out themselves who should be a leader?

28. Let them sort themselves! • Let’s focus on this particular method • Advantage: Any unit can become a leader at any given time, flock will not be leaderless if leader gets destroyed or separated from flock • Once a leader is established, we can implement any number of rules to have the leader do something meaningful • Execute pattern movement or patrolling • Chase or evade or intercept something

29. Leader Check • Can you figure out an algorithm to do this?

30. Leader Check • A possible solution: • Determine the number of units directly in front of or within view of current unit being processed (velocity directions are available for use) • If no other units are directly in front of the unit, it becomes the leader. The rest follows flocking rules • Any more ideas?

31. Follow the Leader • Follow the Leader AI adds an interesting dimension into flocking and group coordinated behavior • More than one leader (of different purposes) can also be implemented • You can also implement flocking behavior for player-friendly/assisting NPCs where the “leader” is simply the player