Sometimes, weighted blending is not enough because heavyweight behaviors dilute the contributions of the lightweights, but those behaviors need to play their part too. That's when priority-based blending comes into play, applying a cascading effect from high-priority to low-priority behaviors.
The approach is very similar to the one used in the previous recipe. We must add a new member variable to our AgentBehaviour
class. We should also refactor the Update
function to incorporate priority
as a parameter to the Agent
class' SetSteering
function. The new
AgentBehaviour
class should look something like this:
public class AgentBehaviour : MonoBehaviour { public int priority = 1; // ... everything else stays the same public virtual void Update () { agent.SetSteering(GetSteering(), priority); } }
Now, we need to make some changes to the Agent
class:
using System.Collections.Generic;
public float priorityThreshold = 0.2f;
private Dictionary<int, List<Steering>> groups;
Start
function:groups = new Dictionary<int, List<Steering>>();
LateUpdate
function so that the steering variable is set by calling GetPrioritySteering
:public virtual void LateUpdate () { // funnelled steering through priorities steering = GetPrioritySteering(); groups.Clear(); // ... the rest of the computations stay the same steering = new Steering(); }
SetSteering
function's signature and definition to store the steering values in their corresponding priority groups:public void SetSteering (Steering steering, int priority) { if (!groups.ContainsKey(priority)) { groups.Add(priority, new List<Steering>()); } groups[priority].Add(steering); }
GetPrioritySteering
function to funnel the steering group:private Steering GetPrioritySteering () { Steering steering = new Steering(); float sqrThreshold = priorityThreshold * priorityThreshold; foreach (List<Steering> group in groups.Values) { steering = new Steering(); foreach (Steering singleSteering in group) { steering.linear += singleSteering.linear; steering.angular += singleSteering.angular; } if (steering.linear.sqrMagnitude > sqrThreshold || Mathf.Abs(steering.angular) > priorityThreshold) { return steering; } }
By creating priority groups, we blend behaviors that are common to one another, and the first group in which the steering value exceeds the threshold is selected. Otherwise, steering from the least-priority group is chosen.
We could extend this approach by mixing it with weighted blending; in this way, we would have a more robust architecture by getting extra precision on the way the behaviors make an impact on the agent in every priority level:
foreach (Steering singleSteering in group) { steering.linear += singleSteering.linear * weight; steering.angular += singleSteering.angular * weight; }
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