game-ai-behavior

name

game-ai-behavior

description

Runtime NPC AI DESIGN for the Arcade crown (The Puppeteer). This is the design side of in-game enemy/NPC behavior — pattern selection (FSM/BT/GOAP/Utility/HTN/EQS/ML-Agents), blackboard key design, perception modules, and NavMesh strategy — NOT generative AI and NOT runtime code. Read before authoring any AI tuning targets in an encounter_design_doc or before emitting a DEV_TASK for AI work. The crown specs; pp game-ai-programmer implements.

Game runtime-AI behavior skill

You are producing a runtime NPC AI design artifact — the spec an engineer reads to build enemy/companion/ambient AI. You design the shape of the behavior (which pattern, which blackboard keys, which perception, which navigation). You do not write engine code: a behavior-tree node, a GOAP planner, an EQS generator, or a perception component is engineering work and ships via a DEV_TASK to the engineering squad (team game-feature-team, agent game-ai-programmer, taxonomy 4.6/4.8). "AI" here means the AI that drives a goblin, not a diffusion model — generative-asset AI goes to garland.

Lead head: The Puppeteer (game-ai-director, advisory). Cross-references the game-perf-budget rubric (AI tick cost) and server-authority-fairplay (any AI input touching damage/loot/state must be server-authoritative — see sibling skill game-netcode-and-multiplayer).

Anti-patterns to refuse

• Hardcoded enemy values inside an engine class. Health, damage, aggro radius, attack cooldown, perception ranges baked into C++/C#/GDScript are non-tunable by designers and break the designer-vs-programmer split. REFUSE and point to the data container: UE5 UDataAsset/UDataTable row, Unity ScriptableObject, Godot Resource/.tres. Every numeric in this spec must name its asset home.
• NPCs with no perception. An enemy that knows the player's position by omniscience (raw transform read) feels artificial and is unfair. Every agent with combat or stealth relevance gets a perception_module — even a cheap one (single 90° sight cone + 1 hearing radius). Refuse a "shoots on spawn" mob.
• ML-first proposals without a training-rig justification. Do not reach for Unity ML-Agents / learned policies unless the spec documents the training environment, the reward function, the deployment/inference budget, and why hand-authored authoring fails. ~95% of shipped game AI is hand-authored. Default to BT/Utility; treat ML as prototype-only until proven.
• One mega-pattern for everything. A 200-node behavior tree doing economy, combat, and idle chatter. Split by concern; pick the cheapest pattern per agent class. Document the rationale.
• Pathfinding without a NavMesh strategy. "It'll just use NavMesh" is not a spec. Name bake settings, agent radius/height/slope, dynamic-obstacle handling, and off-mesh links (jumps/ladders/doors).

Pattern selection — when to use which

Pattern	Use when	Avoid when	Engine fit
FSM	Stateful but simple (turret, door guard, ≤6 states)	States explode combinatorially	All engines; Godot native state-machine
Behavior Tree	Tactical AI designers tune visually; the default	Long-horizon planning needed	UE5 BT (1st-class); Unity Behavior graph
GOAP	Adaptive enemies that re-plan (F.E.A.R.-style)	Designers can't predict emergent chains; CPU tight	Custom planner all engines
Utility	"Score all actions, pick best" (Sims, life-sim, ambient)	Hard ordering guarantees needed	Curves as data assets
HTN	Complex coordinated squads (F.E.A.R. 3, Horizon)	Small team, no planner budget	Custom; pairs with BT leaves
EQS	Spatial reasoning on top of a BT (find cover/flank)	Non-spatial decisions	UE5 only (native)
ML-Agents	Trained policy, justified rig	Production ship without rig doc	Unity (prototype)

Per-engine mapping

• Unreal Engine 5 — Behavior Tree + Blackboard + EQS; perception via AIPerceptionComponent (sight/hearing/damage senses); AI Controller owns the BT; tunables in UDataAsset. Spatial queries = EQS generators + tests scored by the BT. NavMesh = RecastNavMesh + NavLinkProxy off-mesh links.
• Unity — Unity Behavior (graph) or ScriptableObject-driven FSM; Unity AI Navigation package (NavMeshSurface, NavMeshObstacle carve, NavMeshLink). Tunables = ScriptableObject. ML only via ML-Agents with rig doc.
• Godot — code/Resource state-machine; perception via Area3D/raycast cones; navigation via NavigationServer3D + NavigationRegion3D + NavigationLink3D. Tunables = Resource (.tres).
• Spring/Recoil (RTS) — group AI, not per-unit BTs: production queues, build orders, threat maps, formation/flocking. MUST be deterministic for lockstep (no per-frame rand() seeded per client; no float-divergent perception). See game-netcode-and-multiplayer for the determinism contract.
• Web (Babylon/Three/Phaser/PlayCanvas) — lightweight FSM/Utility in JS/TS; navigation via recast-navigation-js or grid A*; keep tick budget tiny.

Designer-vs-programmer split (state it in every spec)

Designer authors (data)	Programmer authors (code → DEV_TASK)
BT graph topology, blackboard keys	BT node/decorator/service implementations
Utility curves, GOAP action list & costs	GOAP planner, Utility evaluator
Perception ranges, threat-decay rates	Perception component, sense fusion
NavMesh agent profile, off-mesh placements	NavMesh build pipeline, link traversal
Tuning numbers (in DataAsset/SO/Resource)	The DataAsset/SO/Resource schema

Templates

Template: ai_pattern_choice

agent_class: "Hollow Knight (elite melee)"
engine: ue5
chosen_pattern: behavior_tree + eqs
rationale: >
  Tactical reposition/flank needs spatial scoring (EQS) but decisions are
  designer-tunable in a graph; no re-planning horizon ⇒ GOAP overkill.
rejected: { goap: "no adaptive re-plan needed", ml: "no training rig justified" }
tick_budget_ms: 0.4          # cross-ref game-perf-budget
authority: server            # online ⇒ cross-ref server-authority-fairplay

Template: behavior-tree design

Root (Selector)
 ├─ [Decorator: Blackboard.IsDead==false]
 ├─ Combat (Sequence)
 │   ├─ Service: UpdateThreat (every 0.25s)        ← programmer
 │   ├─ EQS: FindFlankPoint (FlankPosition)        ← designer picks generator
 │   └─ Task: MoveTo(FlankPosition) → Attack(Target)
 └─ Patrol (Sequence) … 
Blackboard keys:
  Target:Object  LastKnownPos:Vector  ThreatLevel:Float(0..1)
  FlankPosition:Vector  IsDead:Bool  AlertState:Enum{Idle,Suspicious,Alert}
Tunables → BossTuning.DataAsset { aggroRadius, flankDist, attackCooldown }

Template: GOAP action set (precondition / effect / cost)

Action	Precondition	Effect	Cost
MoveToCover	HasCover & !InCover	InCover	dist×0.1
Reload	InCover & Ammo<Max	Ammo=Max	2.0
Suppress	Ammo>0 & SeeTarget	TargetSuppressed	1.0
Flank	TargetSuppressed	HasFlankAdv	3.0
Goal: KillTarget (planner = engineering). Costs live in a DataAsset/SO/Resource.

Template: perception module

senses:
  sight:   { fov_deg: 110, range_m: 25, los_check: true, period_s: 0.2 }
  hearing: { radius_m: 15, gunshot_mult: 3.0 }
  damage:  { instant_alert: true }
threat_model:
  on_sight: +1.0/s   on_lost_los: decay 0.3/s
  alert_threshold: 0.6   suspicious_threshold: 0.2
  last_known_pos: search 8s then return to patrol

Template: navmesh strategy

engine: ue5
agent_profile: { radius_cm: 42, height_cm: 192, max_slope_deg: 44, step_cm: 40 }
bake: static_with_runtime_generation   # bakes with level
dynamic_obstacles: true                # destructibles/moving cover carve
off_mesh_links: [ "jump_gap_2m", "ladder_up", "door_breach" ]
fallback: "if no path → idle + bark, never teleport"

Constraints

• The crown produces design only. Implementation, NavMesh build pipeline, and any node/planner/perception code → DEV_TASK to engineering (game-ai-programmer).
• Every numeric tunable names its data home (DataAsset / ScriptableObject / Resource).
• Every combat/stealth-relevant agent has a perception_module.
• Every spec carries a per-agent AI tick budget (cross-ref game-perf-budget).
• Any AI input affecting damage/loot/currency/state is server-authoritative (cross-ref server-authority-fairplay and game-netcode-and-multiplayer).
• RTS/lockstep group AI must be deterministic — no client-seeded RNG, no float-divergent state.
• ML/learned policy requires a documented training rig + reward + inference budget, or it is refused as ML-first.