unreal-behavior-trees

Unreal Behavior Trees

Author NPC decision-making in UE5 with Behavior Trees driven by a Blackboard: structure the tree with composites, gate branches with decorators, keep state current with services, and run it from an AIController. Targets UE 5.8.

When to use

• Use when building enemy/NPC AI: creating a BT_/BB_ asset pair, structuring Selector/Sequence branches, adding decorators (conditions) and services (periodic updates), writing custom BTTask/BTService nodes, or wiring an AIController to run the tree.
• Use when the project has Behavior Tree (BT_) and Blackboard (BB_) assets and an AAIController.

When not to use: the concept of AI (FSM vs BT vs steering, cross-engine) → game-ai. Pure navigation/pathing math is engine navmesh (BT's MoveTo uses it). Simple one-off logic may be cheaper as a small state machine than a full tree.

Core workflow

1. Create the pair: a Blackboard (BB_) holds typed keys (the AI's memory: TargetActor, LastKnownLocation, bIsInvestigating); a Behavior Tree (BT_) references that Blackboard.
2. Possess and run. An AAIController possesses the pawn and calls RunBehaviorTree(BT), which also initializes the referenced Blackboard.
3. Structure with composites. Selector runs children left→right until one succeeds (priority/fallback: "attack, else chase, else patrol"). Sequence runs children until one fails (do-all: "move to cover → reload → peek"). Simple Parallel runs one main task alongside a secondary.
4. Gate branches with Decorators that read Blackboard keys (e.g. "Has Target?" guards the combat branch). Set Observer Aborts so the tree re-evaluates when the key changes.
5. Keep the Blackboard current with Services attached to a branch — they tick periodically (e.g. update TargetActor via a sight check) only while that branch is active.
6. Do work in Tasks, which return Succeeded, Failed, or InProgress (latent tasks like MoveTo finish later).
7. Verify with the Behavior Tree debugger during PIE — it highlights the running node and shows live Blackboard values, so you see exactly which branch executes.

Patterns

1. AIController that runs the tree (C++)

void AEnemyAIController::OnPossess(APawn* InPawn)
{
    Super::OnPossess(InPawn);

2. A priority tree (node structure)

Observer Aborts: Both makes the combat branch interrupt patrol the instant TargetActor is set, and bail out when it's cleared — this is what makes the AI feel reactive.

3. Updating the Blackboard from code (e.g. on seeing the player)

Pitfalls

• AI never starts — the pawn isn't possessed (set the Pawn's Auto Possess AI to "Placed in World or Spawned" and assign the AIController), or RunBehaviorTree was never called.
• MoveTo instantly fails — no NavMesh in the level (add a Nav Mesh Bounds Volume), or the target is off the navmesh.
• Branch doesn't react to changes — the gating Decorator's Observer Aborts is set to None; set it to Self/Lower Priority/Both so the tree re-evaluates when the key changes.
• A task hangs the tree — a custom task returned InProgress and never calls FinishLatentTask. Always complete latent tasks.
• Blackboard key typos — SetValueAsObject("Taget", ...) silently does nothing; match the key name and type exactly, or use a cached FBlackboardKeySelector.
• Sequence vs Selector confusion — Sequence = AND (stops on first failure); Selector = OR (stops on first success). Swapping them inverts the behaviour.

References

• For a custom C++ UBTTaskNode (instant and latent ExecuteTask returning EBTNodeResult, with a FBlackboardKeySelector), read references/custom-bttask.md.
• Primary docs: "Behavior Trees in Unreal Engine" (https://dev.epicgames.com/documentation/en-us/unreal-engine/behavior-trees-in-unreal-engine).

Related skills

• game-ai — engine-agnostic AI design (FSM, BT, steering, pathfinding choices).
• unreal-cpp-gameplay — the AIController and pawn classes in C++.
• fps-shooter / tower-defense — genres that compose enemy AI.