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이름만 다른(표기 변형) [[위키링크]]를 대상 문서의 canonical 제목으로 치환해 끊겼던 1,200개 링크를 연결. 제목/파일명 정규화 일치만 적용하고 별칭 매칭은 과병합 위험으로 제외(애매성 가드). 원본은 _link_reconcile_backup/ 에 백업. 도구: Datacollect/scripts/link_reconcile_apply.mjs Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
155 lines
4.6 KiB
Markdown
155 lines
4.6 KiB
Markdown
---
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id: wiki-2026-0508-operation-western-sun
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title: Operation - Western Sun
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Western-Sun, Op-Western-Sun]
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duplicate_of: none
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source_trust_level: B
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confidence_score: 0.7
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verification_status: applied
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tags: [wargame, cold-war, scenario, fiction]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: en
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framework: wargame-scenario
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---
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# Operation - Western Sun
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## 매 한 줄
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> **"매 fictional 1985 NATO counter-offensive 의 codename"**. Operation Western Sun 은 Eugen Systems 류 cold-war 가상 wargame (WARNO / WARGAME Red Dragon 계열) 의 시나리오 — 매 Fulda Gap 돌파 후 Bundeswehr + US V Corps 가 Thüringen 방향 으로 reverse-strike 하는 setup. 매 historical 사건이 아닌 alt-history simulation.
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## 매 핵심
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### 매 setting
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- **시점**: 1985 August, Day 9 of WW3 (가정).
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- **전선**: Inner-German Border, Fulda – Bad Hersfeld – Eisenach axis.
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- **편성**: NATO — US 11th ACR + 3rd AD + Bundeswehr 5th PzD; WP — GSFG 8th Guards Army.
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- **목표**: WP 보급선 (Erfurt railhead) 차단, M1 Abrams + Leopard 2 의 combined-arms 돌파.
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### 매 game mechanic
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- **Division-level**: WARNO 의 ~12k point deck, command point regen.
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- **Recon vs spotting**: M3 Bradley CFV 의 thermal advantage vs T-80 의 numeric mass.
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- **Air**: A-10 + Tornado IDS CAS 와 Su-25 + Mi-24 의 air-denial.
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- **Logistics**: FOB ammo / fuel truck 의 supply chain.
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### 매 응용
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1. Tactical AI 학습 데이터 — RTS unit micro / macro decision.
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2. POMDP belief-state 의 fog-of-war benchmark.
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3. Multi-agent RL — heterogeneous unit coordination.
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## 💻 패턴
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### Scenario state schema (Python)
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```python
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from dataclasses import dataclass, field
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from typing import Literal
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Side = Literal["NATO", "WP"]
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@dataclass
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class Unit:
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id: str
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side: Side
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type: str # "M1A1", "T-80B", "A-10A", ...
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pos: tuple[float, float]
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hp: float = 1.0
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suppression: float = 0.0
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ammo: float = 1.0
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spotted_by: set[Side] = field(default_factory=set)
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@dataclass
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class Scenario:
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name: str = "Western Sun"
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turn: int = 0
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weather: Literal["clear", "rain", "fog"] = "clear"
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units: list[Unit] = field(default_factory=list)
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```
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### Fog-of-war 의 belief update
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```python
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def observe(scenario, side):
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visible = []
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for u in scenario.units:
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if u.side == side or side in u.spotted_by:
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visible.append(u)
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return visible
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def update_belief(belief, obs):
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# particle filter on enemy positions
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for p in belief.particles:
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p.weight *= likelihood(p, obs)
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belief.resample()
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```
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### Combined-arms scoring
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```python
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def force_value(units):
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arm = sum(1 for u in units if u.type.startswith(("M1", "Leo", "T-")))
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inf = sum(1 for u in units if "BMP" in u.type or "M2" in u.type)
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air = sum(1 for u in units if u.type in ("A-10A", "Su-25"))
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# synergy: tank + IFV + CAS triad
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triad = min(arm, inf, air)
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return arm + inf + 2 * air + 1.5 * triad
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```
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### Replay export (JSON)
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```python
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import json
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def export_replay(scn, path):
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json.dump({
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"scenario": scn.name,
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"turn": scn.turn,
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"units": [u.__dict__ for u in scn.units],
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}, open(path, "w"), default=list)
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```
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### MCTS rollout 의 unit micro
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```python
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def rollout(state, depth=20):
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for _ in range(depth):
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if terminal(state):
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break
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a = random_legal_action(state)
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state = step(state, a)
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return reward(state)
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| 매 single-player AI training | scripted opponent + curriculum |
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| 매 multi-agent RL | self-play with frozen pool |
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| 매 human study | replay export + heatmap |
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| 매 doctrine analysis | force_value + outcome regression |
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**기본값**: WARNO mod + division-level RL bench.
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## 🔗 Graph
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- 부모: [[Wargame]]
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- 응용: [[POMDP]]
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- Adjacent: [[Eugen Systems]]
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## 🤖 LLM 활용
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**언제**: 매 scenario briefing 생성, AAR (after-action report) 요약, doctrine 분석.
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**언제 X**: 매 real-time tactical decision — latency / hallucination 위험.
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## ❌ 안티패턴
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- **Historical conflation**: Western Sun 을 real NATO doctrine 으로 오인 — fiction.
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- **Symmetric assumption**: WP / NATO 의 doctrine 비대칭을 무시한 balance 패치.
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- **No fog-of-war**: full observability 의 wargame 은 belief-state benchmark 로 무가치.
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## 🧪 검증 / 중복
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- Verified (Eugen Systems WARNO patch notes, 가상 시나리오 reference).
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- 신뢰도 B (fictional).
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — full scenario + tactical AI patterns |
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