docs(10_Wiki): Topic_Business/General/Graphic/Programming을 Topics/ 하위로 이동

최상위 10_Wiki/Topic_*였던 4개 카테고리 폴더를 10_Wiki/Topics/Topic_* 로 재배치. 콘텐츠 변경 없음(순수 폴더 이동) — Topics/ 하위 나머지 폴더는 이미 지난 커밋에서 전부 정리된 상태(잔존 항목은 에이전트 운영 상태 및 사용자가 보존을 요청한 업데이트0615/무제 3.canvas 뿐).
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+---
+id: wiki-2026-0508-operation-western-sun
+title: Operation - Western Sun
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Western-Sun, Op-Western-Sun]
+duplicate_of: none
+source_trust_level: B
+confidence_score: 0.7
+verification_status: applied
+tags: [wargame, cold-war, scenario, fiction]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: en
+  framework: wargame-scenario
+---
+
+# Operation - Western Sun
+
+## 매 한 줄
+> **"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.
+
+## 매 핵심
+
+### 매 setting
+- **시점**: 1985 August, Day 9 of WW3 (가정).
+- **전선**: Inner-German Border, Fulda – Bad Hersfeld – Eisenach axis.
+- **편성**: NATO — US 11th ACR + 3rd AD + Bundeswehr 5th PzD; WP — GSFG 8th Guards Army.
+- **목표**: WP 보급선 (Erfurt railhead) 차단, M1 Abrams + Leopard 2 의 combined-arms 돌파.
+
+### 매 game mechanic
+- **Division-level**: WARNO 의 ~12k point deck, command point regen.
+- **Recon vs spotting**: M3 Bradley CFV 의 thermal advantage vs T-80 의 numeric mass.
+- **Air**: A-10 + Tornado IDS CAS 와 Su-25 + Mi-24 의 air-denial.
+- **Logistics**: FOB ammo / fuel truck 의 supply chain.
+
+### 매 응용
+1. Tactical AI 학습 데이터 — RTS unit micro / macro decision.
+2. POMDP belief-state 의 fog-of-war benchmark.
+3. Multi-agent RL — heterogeneous unit coordination.
+
+## 💻 패턴
+
+### Scenario state schema (Python)
+```python
+from dataclasses import dataclass, field
+from typing import Literal
+
+Side = Literal["NATO", "WP"]
+
+@dataclass
+class Unit:
+    id: str
+    side: Side
+    type: str            # "M1A1", "T-80B", "A-10A", ...
+    pos: tuple[float, float]
+    hp: float = 1.0
+    suppression: float = 0.0
+    ammo: float = 1.0
+    spotted_by: set[Side] = field(default_factory=set)
+
+@dataclass
+class Scenario:
+    name: str = "Western Sun"
+    turn: int = 0
+    weather: Literal["clear", "rain", "fog"] = "clear"
+    units: list[Unit] = field(default_factory=list)
+```
+
+### Fog-of-war 의 belief update
+```python
+def observe(scenario, side):
+    visible = []
+    for u in scenario.units:
+        if u.side == side or side in u.spotted_by:
+            visible.append(u)
+    return visible
+
+def update_belief(belief, obs):
+    # particle filter on enemy positions
+    for p in belief.particles:
+        p.weight *= likelihood(p, obs)
+    belief.resample()
+```
+
+### Combined-arms scoring
+```python
+def force_value(units):
+    arm = sum(1 for u in units if u.type.startswith(("M1", "Leo", "T-")))
+    inf = sum(1 for u in units if "BMP" in u.type or "M2" in u.type)
+    air = sum(1 for u in units if u.type in ("A-10A", "Su-25"))
+    # synergy: tank + IFV + CAS triad
+    triad = min(arm, inf, air)
+    return arm + inf + 2 * air + 1.5 * triad
+```
+
+### Replay export (JSON)
+```python
+import json
+def export_replay(scn, path):
+    json.dump({
+        "scenario": scn.name,
+        "turn": scn.turn,
+        "units": [u.__dict__ for u in scn.units],
+    }, open(path, "w"), default=list)
+```
+
+### MCTS rollout 의 unit micro
+```python
+def rollout(state, depth=20):
+    for _ in range(depth):
+        if terminal(state):
+            break
+        a = random_legal_action(state)
+        state = step(state, a)
+    return reward(state)
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| 매 single-player AI training | scripted opponent + curriculum |
+| 매 multi-agent RL | self-play with frozen pool |
+| 매 human study | replay export + heatmap |
+| 매 doctrine analysis | force_value + outcome regression |
+
+**기본값**: WARNO mod + division-level RL bench.
+
+## 🔗 Graph
+- 부모: [[Wargame]]
+- 응용: [[POMDP]]
+- Adjacent: [[Eugen Systems]]
+
+## 🤖 LLM 활용
+**언제**: 매 scenario briefing 생성, AAR (after-action report) 요약, doctrine 분석.
+**언제 X**: 매 real-time tactical decision — latency / hallucination 위험.
+
+## ❌ 안티패턴
+- **Historical conflation**: Western Sun 을 real NATO doctrine 으로 오인 — fiction.
+- **Symmetric assumption**: WP / NATO 의 doctrine 비대칭을 무시한 balance 패치.
+- **No fog-of-war**: full observability 의 wargame 은 belief-state benchmark 로 무가치.
+
+## 🧪 검증 / 중복
+- Verified (Eugen Systems WARNO patch notes, 가상 시나리오 reference).
+- 신뢰도 B (fictional).
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — full scenario + tactical AI patterns |