In this lesson, you will learn how to map disparate environmental variables—like distance, health, and ammo—into a unified 0.0 to 1.0 range to ensure your AI makes balanced decisions.
Why Normalization Matters
When building utility-based AI, you often compare apples to oranges. For instance, an NPC’s "Distance to Player" might be 50.0 meters, while their "Health Percentage" is 0.8. If your decision logic simply adds these numbers, the distance value will mathematically overpower the health value, causing the NPC to prioritize movement over survival regardless of danger. Normalization allows you to treat every input as a percentage of its theoretical maximum, creating a level playing field for your decision-making algorithms.
Implementing the Remap Function
The most effective way to handle this is by creating a Normalize method that transforms a value from an arbitrary range [min, max] to a standard [0, 1] range.
public float Normalize(float value, float min, float max)
{
// Clamp the value to ensure it stays within bounds
float clampedValue = Mathf.Clamp(value, min, max);
// Calculate the ratio: (Current - Min) / (Range)
return (clampedValue - min) / (max - min);
}
If you have a distance variable where 0 is ideal and 100 is the maximum detection range, passing these into the function ensures that being 25 meters away results in a normalized score of 0.25.
Applying to AI Utility
Once your inputs are normalized, you can safely weight them. If "Health" is critical, you might invert the normalized health (so 0.1 health becomes 0.9 urgency) and multiply it by a high weight factor. Because every variable now exists within the 0.0 to 1.0 space, you can compare a "Desire to Heal" score directly against a "Desire to Attack" score without one variable skewing the results due to its raw magnitude. By standardizing your data, your NPC’s utility calculations become predictable, scalable, and much easier to tune during gameplay testing.
Key Takeaway: Normalizing disparate variables into a 0-to-1 range prevents raw numerical magnitude from biasing your AI's decision-making logic.