2nd/10_Wiki/Topics/AI_and_ML/전투 전술(Battle Strategies).md

---
id: wiki-2026-0508-전투-전술-battle-strategies
title: 전투 전술(Battle Strategies)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Battle Strategies, 전투 전술, Combat Tactics]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [game-design, rts, tactics, combat]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: typescript
  framework: ECS/BehaviorTree
---

# 전투 전술 (Battle Strategies)

## 매 한 줄
> **"매 전투 전술은 unit composition × terrain × tempo 의 곱"**. RTS/wargame design 의 핵심 axis 로, Sun Tzu 의 고전 doctrine 에서 출발해 modern wargame (WARNO, Steel Division 2, Broken Arrow) 에서는 fog-of-war + recon priority + combined-arms 로 진화. AI opponent 는 utility-based scoring 과 behavior tree 로 구현.

## 매 핵심

### 매 4대 축
- **Composition**: unit roster 의 다양성 — infantry / armor / artillery / recon / support
- **Terrain**: cover, elevation, choke point — line-of-sight 와 movement cost 의 변형
- **Tempo**: 매 OODA loop — observe → orient → decide → act, 반복 주기
- **Information**: fog-of-war, recon, EW (electronic warfare) — 정보 비대칭 활용

### 매 doctrine 분류
- **Attrition**: 매 firepower 압박 — losses 를 강제, 적 reinforcement curve 를 break
- **Maneuver**: 매 flanking + encirclement — Schwerpunkt (focal point) 에 mass 집중
- **Defensive-elastic**: 매 layered defense — kill zone 유도 후 counter-attack
- **Asymmetric**: 매 guerilla / hit-and-run — engagement 거부, 공급망 공격

### 매 응용
1. RTS AI controller — utility scoring 으로 매 frame 행동 선택.
2. Wargame mission design — objective gating 으로 player 가 doctrine 학습.
3. Auto-battler balance — 상성 표 + composition entropy 측정.

## 💻 패턴

### Utility-based unit AI
```typescript
type UnitState = { hp: number; ammo: number; pos: Vec2; supply: number }
type Action = "attack" | "retreat" | "hold" | "flank"

function score(s: UnitState, a: Action, ctx: BattleCtx): number {
  switch (a) {
    case "attack":  return s.ammo * 0.4 + ctx.enemyExposed * 0.6 - s.hpDeficit * 0.5
    case "retreat": return s.hpDeficit * 0.8 - ctx.allyNearby * 0.3
    case "hold":    return ctx.cover * 0.7 + ctx.objectiveDist * -0.2
    case "flank":   return ctx.enemyFlankExposed * 0.9 - s.supply * 0.4
  }
}

function pickAction(s: UnitState, ctx: BattleCtx): Action {
  const actions: Action[] = ["attack", "retreat", "hold", "flank"]
  return actions.reduce((best, a) =>
    score(s, a, ctx) > score(s, best, ctx) ? a : best
  )
}
```

### Threat assessment grid
```typescript
function buildThreatMap(units: Unit[], grid: Grid): Float32Array {
  const map = new Float32Array(grid.w * grid.h)
  for (const u of units) {
    if (u.team !== "enemy") continue
    const range = u.weaponRange
    for (let dy = -range; dy <= range; dy++) {
      for (let dx = -range; dx <= range; dx++) {
        const x = u.pos.x + dx, y = u.pos.y + dy
        if (!grid.inBounds(x, y)) continue
        const dist = Math.hypot(dx, dy)
        if (dist > range) continue
        map[y * grid.w + x] += u.firepower * (1 - dist / range)
      }
    }
  }
  return map
}
```

### Schwerpunkt detection
```typescript
function findSchwerpunkt(allies: Unit[], threatMap: Float32Array, grid: Grid): Vec2 {
  let bestScore = -Infinity, bestPos = allies[0].pos
  for (const u of allies) {
    const idx = u.pos.y * grid.w + u.pos.x
    const massNearby = allies.filter(a => Vec2.dist(a.pos, u.pos) < 200).length
    const threatGap = 1 / (threatMap[idx] + 0.1)
    const score = massNearby * threatGap
    if (score > bestScore) { bestScore = score; bestPos = u.pos }
  }
  return bestPos
}
```

### Tempo controller (OODA)
```typescript
class TempoController {
  private lastDecisionAt = 0
  private cycleMs = 800

  tick(now: number, ctx: BattleCtx) {
    if (now - this.lastDecisionAt < this.cycleMs) return
    const obs = this.observe(ctx)
    const orient = this.orient(obs)
    const decision = this.decide(orient)
    this.act(decision)
    this.lastDecisionAt = now
    this.cycleMs = Math.max(300, 800 - ctx.pressureLevel * 100)
  }
}
```

### Recon priority queue
```typescript
function reconTargets(unknown: Cell[], objectives: Vec2[]): Cell[] {
  return unknown
    .map(c => ({
      cell: c,
      score: objectives.reduce((s, o) => s + 1 / (Vec2.dist(c.pos, o) + 1), 0),
    }))
    .sort((a, b) => b.score - a.score)
    .slice(0, 5)
    .map(x => x.cell)
}
```

### Combined-arms morale boost
```typescript
function moraleBonus(unit: Unit, allies: Unit[]): number {
  const nearby = allies.filter(a => Vec2.dist(a.pos, unit.pos) < 150)
  const types = new Set(nearby.map(a => a.type))
  return types.size >= 3 ? 0.25 : types.size >= 2 ? 0.1 : 0
}
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Numeric superiority + open terrain | Attrition + frontal mass |
| Mobile force vs static defense | Maneuver, flank Schwerpunkt |
| Outnumbered, defensive | Elastic defense + kill zones |
| Unknown enemy composition | Recon-heavy, delay engagement |
| Long campaign, supply-limited | Asymmetric, denial |

**기본값**: combined-arms balanced doctrine + recon-first opening.

## 🔗 Graph
- 부모: [[제병협동 (Combined Arms)]] · [[Game_Design]]
- 변형: [[유닛 상성(Unit Counters)]] · [[제로잉 (Getting Zero-ed)]]
- 응용: [[WARNO]] · [[Eugen_Systems]] · [[4X_전략]]
- Adjacent: [[AI 추적 논리(AI Pursuit Logic)]] · [[Combat_Timeline_Difficulty_Scaling]]

## 🤖 LLM 활용
**언제**: tactical AI scripting, mission designer 의 pacing 검증, opening-book 생성.
**언제 X**: 매 frame-tight pathfinding 의 inner loop — LLM latency 가 너무 큼, behavior tree 에 위임.

## ❌ 안티패턴
- **Single-unit doctrine**: 단일 병종 spam — counter unit 1 종류로 전체 collapse.
- **Static formation**: tempo 무시 — 매 적의 OODA 가 빨라 reactive 가 됨.
- **Recon skip**: 매 fog-of-war 무시 → blind commit, ambush 함정.
- **Schwerpunkt 부재**: 매 force dispersion → 어디서도 decisive mass 못 만듦.

## 🧪 검증 / 중복
- Verified (Eugen Systems WARNO design docs, US FM 3-0 Operations).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — battle tactics doctrine + AI scoring patterns |