2nd/10_Wiki/Topics/AI_and_ML/Operation- Western Sun.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-operation-western-sun	Operation - Western Sun	10_Wiki/Topics	verified	self	Western-Sun Op-Western-Sun	none	B	0.7	applied	wargame cold-war scenario fiction		2026-05-10	pending	language framework en 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)

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

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

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)

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

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