ai-behavior-trees-utility-ai

Behavior Trees & Utility AI

Two complementary ways to structure NPC decision-making, plus how to combine them. A behavior tree (BT) expresses structured, prioritized, reactive logic as a tree that is "ticked" each step. Utility AI answers "how much do I want each option right now?" by scoring actions with normalized curves and picking the best. Ship believable agents by using a BT for structure and Utility AI where graded trade-offs matter.

This skill is the implementation companion to game-ai (which helps you choose between FSM / BT / steering / pathfinding). Read game-ai to pick a model; read this to build the runtime.

When to use

• Use to build a reusable BT runtime: a Blackboard, Node base, action/condition leaves, Sequence/Selector/Parallel composites, and decorators (Inverter, Cooldown, Repeat).
• Use to build a Utility AI decider: response curves, considerations, and an evaluator that scores and selects actions (max, softmax, or weighted-random for variety).
• Use to build hybrid AI — a BT whose leaf delegates the "which attack / which target" choice to a utility evaluator.

When not to use: to choose between FSM, BT, steering, or pathfinding, and for A*/navmesh routing, use game-ai. For Unreal's asset-based BehaviorTree/Blackboard, BTTask/BTService and AIController, use unreal-behavior-trees. For the navmesh agent that moves the NPC, use unity-navmesh or the engine's navigation node.

Core workflow

1. Pick the model. Structured, prioritized, interruptible behavior → BT. Continuous "score every option" decisions (targeting, needs, item choice) → Utility. Both → hybrid.
2. Design the Blackboard first. One typed key/value store per agent is the shared memory that decouples nodes; leaves read/write it and never hold references to each other.
3. Write leaves. Conditions return Success/Failure immediately; actions return Running across frames until they finish. Keep leaves small and side-effect-explicit.
4. Compose. Selector = OR/fallback (first non-failure wins); Sequence = AND (stop at first non-success); Parallel for concurrent branches. Wrap with decorators for policy (invert, cooldown, repeat, force-success).
5. For Utility: enumerate considerations, map each raw fact through a normalized 0..1 curve, combine (weighted product with compensation, or weighted sum), then select the max — add hysteresis so agents don't flip-flop on ties.
6. Tick deliberately. Tick the tree/evaluator once per decision step (often slower than render). Preserve Running state between ticks; verify by drawing the active path and the per-action scores on screen while tuning.