AI Techniques

AI Techniques For Games:

Boids Flocking:

In the natural world, organisms exhibit certain behaviours when travelling in groups. This phenomenon, also known as flocking, occurs at both microscopic scales (bacteria) and macroscopic scales (fish). (Gallear, A.I Techniques, 2018) It is based on three-dimensional computational geometry of the sort normally used in computer animation or computer aided design. The basic simulated flocking creatures were designated “Boids” and the standard flocking model is made up of three steering behaviours which detail how an individual “Boid” manoeuvres based on the positions and velocities of its nearby flock mates. (Reynolds, 2018). By using computers, these patterns can be simulated by creating simple rules and combining them. This is known as emergent behaviour and can be used in games to simulate chaotic or lifelike group movement. (Gallear, A.I Techniques, 2018). This technique works on three simple components known as cohesion, separation and alignment.

Cohesion:

A flock is when a group of boids all stay close together. Cohesion is when each boid will look at the position of every other boid to see if it is within the neighbour radius. If a fellow boid is within the radius than it becomes a flock mate. It than takes the average position of each boid and will steer toward that position. This makes it so each boid is trying to get to the centre resulting in them staying together. I.E: Steer to move toward the average position of local flockmates.

Separation:

Even though each boid is endeavouring to draw near together you don't need them covering or hitting against as this doesn't this doesn’t simulate real life which is the place the partition some portion of the calculation comes in. They do this by having each boid keep a specific measure of room in the middle of one another. In the event that a boid gets excessively near another boid than it will add a converse speed to itself to go the other way. I.E: Steer to avoid crowding local flockmates.

Alignment:

This is the part of the algorithm which oversees the direction of the flock. The way it works is that every frame each boid will look at the heading it which it is heading and those of its flock mate’s aka those inside neighbour radius. It will than realign itself to match the heading of all its neighbours. It than averages the velocity vectors of those in the neighbour radius and the resulting vector point is the way than the boid will head towards. I.E: Steer towards the average heading of local flockmates.