Utility AI Decision Engine — NPC Behavior Simulator

Decision Tuning

Need response curve (exponent) 2.0

Threat sensitivity 1.0

Decision noise 0.15

Simulation speed 1.0×

Live Action Mix

Avg hunger

Avg fatigue

Sim time

0.0s

Meals eaten

How it works

Utility AI is a decision architecture used for NPC brains (The Sims, XCOM, many strategy games). Instead of a fixed if/else tree, every candidate action gets a numeric score every tick, and the agent simply does whichever action scores highest right now:

U(Forage) = hunger^k + 0.25 · proximity(nearestFood)
U(Rest)   = fatigue^k + 0.25 · proximity(restZone)
U(Flee)   = s · max(0, 1 − dist(threat) / R)   [only if threat is near]
U(Wander) = 0.15                                [idle floor, always available]

score = U(action) + noise · (rand() − 0.5)
choose action = argmax(score)

k — response curve exponent. A low exponent (~1) makes agents react to a need as soon as it starts rising. A high exponent (~4) makes them ignore it almost entirely until it is nearly maxed out, then react sharply — this curve shape is the actual tuning knob game designers expose to balance NPC behavior.
s — threat sensitivity scales how strongly a nearby threat overrides everything else, inside a fixed detection radius R.
Decision noise adds small random jitter to every score, so near-ties don't all resolve identically — this keeps a crowd of agents from looking robotically synchronized, at the cost of some sub-optimal choices.
Each agent tracks its own hunger and fatigue, which grow over time and reset when the agent reaches a food node or the rest zone — so the same utility formula produces different observed behavior per agent purely from their current need state.

Forage Rest Flee Wander

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