docs(10_Wiki): 위키 전체 재구성 — Topic_* 폴더를 4개 카테고리로 통합 + 대규모 중복 제거
Topic_Agent/Topic_Blog/Topics/Topics_Biz/Topics_Meeting/Topics_Rag의 마크다운 지식 문서를 Topic_General/Topic_Programming/Topic_Graphic/Topic_Business 4개 카테고리로 재분류. - 중복 제거: frontmatter의 status:duplicate/merged + duplicate_of/redirect_to 필드로 자기 자신을 중복으로 선언한 리다이렉트 stub 1032개 제거, 완전 동일 내용 파일 472개 제거, 동일 파일명·다른 내용 충돌 시 더 큰(완전한) 버전만 유지(162개 제거) — 총 1639개 중복 제거. - 분류: 폴더 단위로 명확한 항목(AI_and_ML/Coding/Architecture 등 → Programming, Comfyui/Visual_Effects → Graphic, Topics_Biz/Topics_Meeting/사업 등 → Business, Poetic_Blog_Writing/창의성/Game_Design 등 → General)은 폴더 우선순위로, 나머지 혼재 폴더(Topic_Agent/Topic_Blog/Topics 루트/Thinking & Reasoning/Other/UI_UX_Assets)는 title/tags 키워드 스코어링으로 파일 단위 분류(불명확한 경우 General로 폴백). 원본 폴더명은 "From_*" 서브폴더로 보존해 추적 가능성 유지. - 최종 배치: Programming 2784 / General 1608 / Graphic 285 / Business 249 = 4926개 문서. - 에이전트 운영 상태(.astra/.agent/.obsidian/sessions/memory/_company/docs/lessons/_shared/src)는 지식 콘텐츠가 아니므로 재분류 대상에서 제외하고 원위치 유지. - Topics/Topic_email(상위 보호 폴더 Topic_email과 파일명 100% 중복) 삭제 — 보호 폴더 자체는 미변경. - 완전히 비게 된 Topic_Agent/Topic_Blog/Topics_Biz/Topics_Rag 폴더 제거.
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Antigravity Agent
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---
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id: wiki-2026-0508-dda
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title: Dynamic Difficulty Adjustment (DDA)
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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: [DDA, dynamic difficulty, rubber banding, AI Director, flow state, adaptive difficulty]
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duplicate_of: none
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source_trust_level: B
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confidence_score: 0.85
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verification_status: applied
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tags: [game-design, dda, dynamic-difficulty, flow, ai-director, player-modeling, adaptive]
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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: game design
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applicable_to: [Game Design, Adaptive Learning, ML Curriculum]
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---
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# Dynamic Difficulty Adjustment (DDA)
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## 매 한 줄
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> **"매 player skill 의 real-time 매 difficulty 의 dial"**. Csikszentmihalyi 의 Flow zone 의 maintain. 매 Left 4 Dead 의 AI Director, 매 racing 의 rubber banding. 매 modern: 매 ML-driven player model + 매 ethical detect (gaming the system). 매 LMS adaptive learning 의 same principle.
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## 매 핵심
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### 매 mechanism
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1. **Player metric**: 매 win rate, HP remaining, time, deaths.
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2. **Skill estimate**: 매 sliding window of recent.
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3. **Adjustment**: 매 enemy stats / spawn / hint.
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4. **Subtle**: 매 obvious 의 player 의 frustrate.
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### 매 famous example
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- **Left 4 Dead AI Director** (Valve): 매 zombie spawn 의 control.
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- **Resident Evil 4**: 매 difficulty 의 silent.
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- **Mario Kart**: 매 rubber banding (item, speed).
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- **Crash Bandicoot**: 매 attempt 후 의 adjust.
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- **Mario AI** (research): 매 student 의 player.
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### 매 method
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- **Heuristic**: 매 simple rule.
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- **Bayesian player model**: 매 skill posterior.
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- **RL-based**: 매 difficulty 의 policy 학습.
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- **MCTS-based**: 매 lookahead.
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### 매 ethical / design constraint
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- **Subtle**: 매 player 의 perception X.
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- **Fair feel**: 매 effort-reward.
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- **Not patronize**: 매 매 player 의 skill ↑ 의 acknowledge.
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- **No gaming detection**: 매 player 의 intentional poor 의 abuse.
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### 매 응용
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1. **Action / shooter**: 매 enemy difficulty.
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2. **Racing**: 매 rubber banding.
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3. **Puzzle**: 매 hint.
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4. **MMO raid**: 매 boss adjustment.
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5. **Edu game / LMS**: 매 question difficulty.
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6. **AI tutor**: 매 explanation depth.
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7. **Aim training**: 매 [[Cognitive Training Software (eg Aim Lab_KovaaKs)]] 의 adaptive scenario.
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### 매 modern AI 응용
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- **Player modeling**: 매 ML 의 skill 의 estimate.
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- **RL-based DDA**: 매 difficulty controller 의 train.
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- **Generative content**: 매 procedurally adapted level.
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## 💻 패턴
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### Sliding window skill estimator
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```ts
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class SkillEstimator {
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private history: number[] = [];
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recordOutcome(win: boolean, time: number, hpLost: number) {
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const score = (win ? 1 : 0) * (60 / Math.max(time, 1)) * (1 - hpLost / 100);
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this.history.push(score);
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if (this.history.length > 20) this.history.shift();
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}
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estimateSkill(): number {
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if (!this.history.length) return 0.5;
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return this.history.reduce((a, b) => a + b, 0) / this.history.length;
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}
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}
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```
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### AI Director-style (probabilistic)
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```ts
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class AIDirector {
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threatLevel = 0; // 0-1
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update(player: Player) {
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const recentDamage = player.recentDamageTaken();
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const ammoLow = player.ammo < 30;
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const downtime = player.timeWithoutCombat();
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// 매 raise threat 의 calm
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if (downtime > 30) this.threatLevel = Math.min(1, this.threatLevel + 0.05);
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// 매 ease 의 stress
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if (recentDamage > 50 || ammoLow) this.threatLevel = Math.max(0, this.threatLevel - 0.1);
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}
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shouldSpawnEnemy(): boolean {
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return Math.random() < this.threatLevel;
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}
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enemyComposition() {
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if (this.threatLevel < 0.3) return 'easy_pack';
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if (this.threatLevel < 0.7) return 'mixed';
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return 'horde';
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}
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}
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```
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### Rubber banding (racing)
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```ts
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function applyRubberBand(car: Car, leader: Car) {
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const distanceBehind = leader.position - car.position;
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// 매 subtle catch-up
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if (distanceBehind > 100) {
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car.maxSpeed *= 1.05; // 매 5% boost
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car.itemDropChance *= 1.5; // 매 better items
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} else if (distanceBehind < -50) {
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// 매 leader 의 small handicap
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car.maxSpeed *= 0.98;
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}
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}
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```
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### Bayesian player model (IRT-inspired)
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```python
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import numpy as np
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class BayesianPlayer:
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def __init__(self, prior_mean=0, prior_var=1):
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self.skill_mean = prior_mean
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self.skill_var = prior_var
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def update(self, item_difficulty, success):
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"""매 item response theory."""
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# 매 expected probability
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p_success = 1 / (1 + np.exp(-(self.skill_mean - item_difficulty)))
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# 매 posterior update (simplified Kalman)
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info = p_success * (1 - p_success)
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self.skill_var = 1 / (1 / self.skill_var + info)
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self.skill_mean += self.skill_var * info * (int(success) - p_success)
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def select_difficulty(self, items, target_p=0.7):
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"""매 just-beyond comfort."""
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return min(items, key=lambda d: abs(
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1 / (1 + np.exp(-(self.skill_mean - d))) - target_p
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))
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```
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### RL-based DDA
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```python
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class DifficultyController:
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"""매 RL 의 매 player 의 engagement 의 maximize."""
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def __init__(self):
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self.policy = QNetwork()
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def select_difficulty(self, player_state):
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actions = ['decrease', 'maintain', 'increase']
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q_values = self.policy(player_state)
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return actions[q_values.argmax()]
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def reward(self, player_state):
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# 매 engagement = match length + retention + flow indicator
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return (
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player_state.session_length * 0.3 +
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(1 if player_state.continued_after_match else 0) * 0.5 +
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player_state.subjective_satisfaction * 0.2
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)
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```
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### Detect gaming the system
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```python
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def detect_throw_match(player_history):
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"""매 player 의 intentional poor 의 detect."""
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recent = player_history[-10:]
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# 매 sudden drop in performance
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historical_avg = np.mean([h.score for h in player_history[:-10]])
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recent_avg = np.mean([h.score for h in recent])
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if recent_avg < historical_avg * 0.4:
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return 'WARN: possible throwing for DDA exploit'
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return None
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```
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### Subtle communication (UX)
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```ts
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function hidePlayerSeesAdjustment() {
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// 매 ❌ 매 obvious
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if (showText) showMessage('Difficulty decreased due to your performance');
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// 매 ✅ 매 silent
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// 매 just adjust enemy stats internally.
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// 매 player 의 say "I'm getting better!" 매 actually 의 difficulty 의 ease.
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}
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```
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### A/B test (DDA effectiveness)
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```python
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def ab_test_dda(players_a_no_dda, players_b_with_dda):
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return {
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'session_length_a': mean(p.session_length for p in players_a_no_dda),
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'session_length_b': mean(p.session_length for p in players_b_with_dda),
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'retention_d7_a': retention(players_a_no_dda, days=7),
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'retention_d7_b': retention(players_b_with_dda, days=7),
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'satisfaction_a': mean(p.satisfaction for p in players_a_no_dda),
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'satisfaction_b': mean(p.satisfaction for p in players_b_with_dda),
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}
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```
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### LMS adaptive (similar pattern)
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```python
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def adaptive_quiz(student, item_pool):
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skill = student.estimated_skill
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# 매 zone of proximal development
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target_difficulty = skill + 0.5 # 매 just-beyond
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next_item = min(item_pool, key=lambda i: abs(i.difficulty - target_difficulty))
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response = present(next_item, student)
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student.update_skill(next_item.difficulty, response.correct)
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return next_item
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```
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### Procedural difficulty (level generation)
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```python
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def generate_level(player_skill):
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return {
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'enemy_count': int(10 + player_skill * 20),
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'enemy_hp': 100 * (1 + player_skill * 0.5),
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'puzzle_complexity': int(player_skill * 5),
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'time_limit': 300 - int(player_skill * 100),
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'powerup_density': max(0.1, 0.5 - player_skill * 0.3),
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}
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```
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## 매 결정 기준
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| Genre | DDA approach |
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|---|---|
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| Action / Shooter | AI Director (Valve-style) |
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| Racing | Rubber banding (subtle) |
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| Puzzle | Hint frequency |
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| MMO raid | Stat-tier difficulty |
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| RPG | Side content |
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| Edu game | Adaptive item (IRT) |
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| Esports / competitive | NO DDA (fairness) |
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| Casual mobile | Subtle DDA + retention focus |
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**기본값**: 매 Bayesian player model + 매 subtle adjust + 매 A/B test + 매 detect gaming.
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## 🔗 Graph
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- 부모: [[Game-Design]] · [[Adaptive-Learning]]
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- 변형: [[AI-Director]] · [[Rubber-Banding]] · [[Player-Modeling]]
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- 응용: [[Cognitive Training Software (eg Aim Lab_KovaaKs)]] · [[Corporate-LMS-Training]] (adaptive) · [[Cognitive-Evaluation-Theory]]
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- Adjacent: [[Default Mode Network (DMN)]] (flow) · [[Deliberate-Practice]] · [[Combined Arms (제병협동) 전술]]
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## 🤖 LLM 활용
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**언제**: 매 game design. 매 adaptive learning system. 매 personalized challenge.
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**언제 X**: 매 esports / fairness-critical (no DDA).
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## ❌ 안티패턴
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- **Obvious DDA**: 매 player 의 perception → 매 motivation lose.
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- **Patronizing**: 매 매 player 의 skill 의 acknowledge X.
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- **Punishing skilled play**: 매 better → 매 harder feel of unfair.
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- **No detection 의 throw**: 매 exploit.
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- **DDA in competitive**: 매 fairness violation.
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- **Too aggressive adjust**: 매 whiplash.
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## 🧪 검증 / 중복
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- Verified (Csikszentmihalyi Flow, Valve AI Director paper, Hunicke MDA).
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- 신뢰도 B.
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- Related: [[Cognitive Training Software (eg Aim Lab_KovaaKs)]] · [[Corporate-LMS-Training]] · [[Cognitive-Evaluation-Theory]] · [[Deliberate-Practice]] · [[Combined Arms (제병협동) 전술]].
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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 — mechanism + 매 AI Director / rubber band / Bayesian / RL / detect-throw code |
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