enemy-ai-framework

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Architecture blueprint for game AI across genres: designer-authorable decision architectures (FSM/HSM vs behavior trees vs GOAP vs HTN vs utility/IAUS — the decision matrix), the brain-to-intent-to-execution separation (AI drives the same character controller and combat as the player), perception (sight cones, hearing propagation, the alert ladder), threat and target selection with hysteresis, attack tokens as the pacing/difficulty regulator, lifecycle (spawn, patrol, leash, respawn), AI LoD and crowd scale, group coordination, genre AI beyond action (RTS command hierarchy and influence maps, stealth alert states and Alien Isolation's two-brain model, racing Drivatar imitation, sim smart- objects and needs, director/macro AI, companion AI, the Nemesis social system), the technical foundations (navmesh/Recast, pathfinding and the funnel, RVO/ORCA avoidance, steering and context-steering, influence maps and EQS, ML reality vs hype, crowds via flow fields and Mass/DOTS), and AI believability (the fun-vs- smart t

Firzus By Firzus schedule Updated 6/11/2026

name: enemy-ai-framework description: >- Architecture blueprint for game AI across genres: FSM/HSM, behavior trees, GOAP/HTN/utility AI, brain-intent-execution separation, perception, aggro, threat selection, attack tokens, lifecycle, AI LoD, group coordination, pathfinding, avoidance, crowds, and believability. Use when designing enemy AI, behavior trees, spawning, stealth/RTS/racing/sim AI, or when enemies swarm, flip targets, path badly, or feel robotic.

Enemy AI Framework

Build the AI layer of a game. References: Genshin's GDC 2021 scalable AI (200+ designer-authored archetypes), the attack-token model (Doom 2016, God of War), F.E.A.R.'s GOAP and the "illusion of intelligence", Halo's archetype sandbox, souls-like aggro/leash. Excluded (covered elsewhere): boss phase data (combat-system), town-NPC schedules.

The two architecture rules

  1. The brain decides; the body is the player's. AI emits intents — MoveTo(pos), Attack(target, skillId) — consumed by the same character controller and combat system as the player. Payoff: staggers/knockbacks work identically, no movement cheating, hitboxes implemented once, and you can plug an AI brain into the player character for soak tests. ("Have the AI press buttons.")
  2. Decision architecture by role, designer-authorability as the deciding criterion (Genshin rejected one big BT for productivity):
FSM/HSM        → lifecycle/context states (idle/patrol/combat/return, boss phases)
Decision trees → action selection INSIDE a state (light, stateless, recomposable)
  or BT
Utility/IAUS   → continuous parametric choices (target, skill, position)
GOAP / HTN     → only when multi-step improvisation IS the fantasy

Genshin's shipped pipeline (per-frame, modular): Sensing → Threat → Target Select → Reactions → Scripted → Group → Positioning — modules recompose per archetype; designers author new enemies without engineers.

Reference map

File Covers
decision-perception.md The decision-architecture comparison settled by designer authorability, the intent bridge (brain → body), perception (sight/hearing/the alert ladder), threat and target selection with hysteresis
combat-tokens.md Attack tokens (the pacing/difficulty regulator), telegraphs and the fairness floor, lifecycle (spawn/patrol/leash/respawn), AI LoD and the bubble, group coordination
genres.md AI beyond action: RTS command hierarchy + influence maps + the cheating reality, stealth alert states + Alien Isolation's two-brain model, racing Drivatar imitation, sim smart-objects/needs, director/macro AI (Left 4 Dead), companion AI (Elizabeth/Ellie), the Nemesis social system
techniques.md The decision-architecture matrix (FSM/BT/GOAP/HTN/utility), navigation (Recast navmesh, the funnel, HPA*), RVO/ORCA avoidance, steering and context-steering, influence maps and EQS, ML reality vs hype, crowds via flow fields and Mass/DOTS
believability.md The fun-vs-smart thesis, the illusion of intelligence via barks, difficulty as behavior not stats, believable imperfection and the fairness contract, archetypes and encounter composition, reactivity and juice
pitfalls.md 16 failure modes (symptom → cause → prevention) with debugging order and ship checklist

Attack tokens (the pacing regulator)

One system controls difficulty AND readability:

  • N tokens per encounter; an enemy claims before attacking, releases after (end, whiff, interruption, death — release in state teardown, never just the happy path, plus a safety timeout). Separate melee and ranged pools.
  • Off-token enemies look busy: reposition, strafe, flank, posture, taunt — never statically wait (GoW's "kung fu circle"). Doom lets demons steal tokens; GoW lets an interrupted enemy keep its token briefly (anti-starvation).
  • Difficulty scales by tokens, not HP: more tokens + faster redemptions = harder, instead of bullet sponges. Full detail in combat-tokens.md.

Build order (4 shippable tiers)

Tier 1 — One enemy that fights fair
- [ ] HSM lifecycle + decision tree for attacks; the intent bridge
- [ ] Perception: sight cone + LoS + hearing + hit = aggro
- [ ] DEBUG OVERLAY FROM DAY 1 (the #1 productivity item)
Tier 2 — Encounters
- [ ] Attack tokens (pools, guaranteed release, timeout)
- [ ] Threat model + ratio hysteresis; slot-based positioning (no conga lines)
- [ ] Leash as a brain state; telegraphs with the fairness floor
Tier 3 — World integration
- [ ] Spawners + persistent state by stable IDs; AI LoD (3 tiers, per-module)
- [ ] Streaming guards; the alert ladder + group alarm propagation
Tier 4 — Texture & scale
- [ ] Group roles with caps; per-instance personality (kill "identical robots")
- [ ] Archetype roster + encounter composition (rock-paper-scissors)
- [ ] Barks announcing intent (the believability multiplier); difficulty by behavior

Key numbers (starting points — tune by playtest)

Parameter Value Anchor
Sight cone 120° H × 60° V; 20–30 m fodder (200 m in combat) Genshin datamine
Threat +20 per hit, decay ~3%/s, switch at ≥120% ratio (NOT time-based) Genshin/WoW
Tokens 1 melee + 1–2 ranged; counts unpublished convention
Telegraphs 0.4 s light / 0.6–0.8 s heavy / ≥1 s boss (≥0.3–0.5 s floor) design refs
AI budget Genshin ships 30+ NPCs at 0.5 ms on mobile after LoD GDC 2021
L4D Director adjusts pacing not difficulty (intensity → Build/Sustain/Fade/Relax) Booth GDC 2009
RTS cheating SC2 levels 8–10 cheat (full vision + harvest boost) SC2LE/TStarBots
Halo barks 57 events, 166 dialogue types, 5,147 lines Griesemer GDC 2002

Full sourced tables (with the "undocumented — don't invent" list) in each file.

Engine mapping (summary)

Generic block Unity 6 UE5 (5.4+)
Brain Unity Behavior (frozen) · custom HSM/BT · Behavior Designer BT + Blackboard (event-driven) · StateTree
Perception custom: OverlapSphere + LoS + stimulus bus AIPerception (sight/hearing/damage, time-sliced)
Intent bridge updatePosition=false + desiredVelocity → shared controller AIController/Pawn split is native
Navigation AI Navigation (Recast) + A* Pathfinding Project Navmesh + Detour Crowd (RVO) + nav links
Spatial queries DIY / assets EQS (generators + tests)
Tokens custom token manager custom; Smart Objects as a claim primitive
Crowds DOTS/ECS + Burst + flow fields Mass (MassEntity + ZoneGraph + StateTree)
ML ML-Agents (mostly QA/playtest) Learning Agents (experimental)

UE5 ships ~5 of the 6 blocks natively; the one block custom everywhere: attack tokens. Full detail in techniques.md.

Related skills

  • character-controller / combat-system — the body: AI intents drive these; staggers and hit events flow back to the brain.
  • open-world-streaming — AI residency at cell borders, the bubble model.
  • world-time-weather — time divisions and weather flags as blackboard inputs.
  • adaptive-audio — the aggro/threat ladder triggers combat music.
  • loot-drop-system — death hands off to the drop pipeline; tier substitution.
  • coop-session — server-owned AI, per-player threat tables.
  • dialogue-system — the LLM-NPC frontier; combat barks share the bark arbiter.
  • game-architecture-patterns — State (HSM), Type Object (archetypes), Event Queue (stimuli), Update Method (LoD) theory.
Install via CLI
npx skills add https://github.com/Firzus/agent-skills --skill enemy-ai-framework
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