Enemy Ai Framework

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.")

1. **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](./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](./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](./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](./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](./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](./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](./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](./techniques.md).

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Firzus
• Source: Firzus/agent-skills
• License: MIT

Install and usage instructions live in the source repository linked above.