refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조

에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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+---
+id: wiki-2026-0508-emotional-ai-affective-computing
+title: Emotional AI (Affective Computing)
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [affective computing, emotion AI, sentiment analysis, emotion recognition, FER]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.95
+verification_status: applied
+tags: [ai, affective-computing, emotional-ai, sentiment, fer, multimodal, hci]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Python
+  framework: PyTorch / Transformers / DeepFace
+---
+
+# Emotional AI (Affective Computing)
+
+## 매 한 줄
+> **"매 emotion 의 sense + 매 generate"**. Picard MIT (1997). 매 facial, voice, text, physiological. 매 modern: 매 multimodal LLM + valence-arousal regression. 매 ethical: 매 surveillance + cultural bias.
+
+## 매 핵심
+
+### 매 emotion model
+- **Discrete** (Ekman): 매 6 basic — anger, fear, disgust, surprise, sadness, joy.
+- **Dimensional** (Russell): 매 valence × arousal.
+- **Plutchik wheel**: 매 8 + intensity.
+- **Appraisal** (Scherer): 매 cognitive evaluation.
+
+### 매 modality
+- **Facial** (FER): 매 AU (action unit).
+- **Voice**: 매 prosody, pitch.
+- **Text**: 매 lexicon + transformer.
+- **Physiological**: 매 HRV, GSR, EEG.
+- **Body**: 매 posture, gait.
+- **Multimodal**: 매 fuse.
+
+### 매 응용
+1. **Customer service**: 매 sentiment.
+2. **Mental health**: 매 mood track.
+3. **Education**: 매 engagement.
+4. **Auto**: 매 driver drowsiness.
+5. **Marketing**: 매 ad reaction.
+6. **Robot**: 매 emotional support.
+7. **Game**: 매 dynamic difficulty.
+
+### 매 ethical
+- **Privacy**: 매 emotion 의 surveil.
+- **Cultural bias**: 매 Ekman universality 의 contested.
+- **Inaccuracy**: 매 expression ≠ feeling.
+- **Manipulation**: 매 ad / dark pattern.
+- **Regulation**: 매 EU AI Act 의 emotion 의 high-risk.
+
+## 💻 패턴
+
+### Text sentiment (transformers)
+```python
+from transformers import pipeline
+pipe = pipeline('sentiment-analysis', model='cardiffnlp/twitter-roberta-base-sentiment')
+print(pipe("I love this!"))
+# 매 [{'label': 'POSITIVE', 'score': 0.99}]
+```
+
+### Emotion classification (text, 7-class)
+```python
+classifier = pipeline('text-classification', model='j-hartmann/emotion-english-distilroberta-base', top_k=None)
+result = classifier("I'm worried about tomorrow")
+# 매 [{'label': 'fear', 'score': 0.7}, ...]
+```
+
+### Facial emotion (DeepFace)
+```python
+from deepface import DeepFace
+result = DeepFace.analyze('selfie.jpg', actions=['emotion'])
+print(result[0]['dominant_emotion'])  # 매 'happy'
+```
+
+### Voice emotion (audio + Wav2Vec2)
+```python
+import librosa
+import torch
+from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor
+
+model = Wav2Vec2ForSequenceClassification.from_pretrained('superb/wav2vec2-base-superb-er')
+fe = Wav2Vec2FeatureExtractor.from_pretrained('superb/wav2vec2-base-superb-er')
+
+def predict_emotion(audio_path):
+    audio, sr = librosa.load(audio_path, sr=16000)
+    inputs = fe(audio, sampling_rate=16000, return_tensors='pt')
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    return model.config.id2label[logits.argmax().item()]
+```
+
+### Valence-Arousal regression
+```python
+class VARegressor(nn.Module):
+    def __init__(self, backbone):
+        super().__init__()
+        self.backbone = backbone
+        self.va_head = nn.Linear(backbone.hidden_dim, 2)  # 매 valence, arousal
+    
+    def forward(self, x):
+        feat = self.backbone(x)
+        va = torch.tanh(self.va_head(feat))  # 매 [-1, 1]
+        return va  # 매 [batch, (valence, arousal)]
+```
+
+### Multimodal fusion
+```python
+class MultiModalEmotion(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.face_enc = FacialEncoder()
+        self.audio_enc = AudioEncoder()
+        self.text_enc = TextEncoder()
+        self.fusion = nn.Linear(768 * 3, 7)
+    
+    def forward(self, face, audio, text):
+        ff = self.face_enc(face)
+        fa = self.audio_enc(audio)
+        ft = self.text_enc(text)
+        return self.fusion(torch.cat([ff, fa, ft], dim=-1))
+```
+
+### Physiological (HRV stress)
+```python
+import neurokit2 as nk
+def stress_from_ecg(ecg_signal, sr=1000):
+    signals, info = nk.ecg_process(ecg_signal, sampling_rate=sr)
+    hrv = nk.hrv_time(signals, sampling_rate=sr)
+    rmssd = hrv['HRV_RMSSD'].iloc[0]
+    return 'stressed' if rmssd < 30 else 'calm'
+```
+
+### Cultural-aware (avoid bias)
+```python
+def culturally_aware_predict(image, region):
+    base_pred = model.predict(image)
+    # 매 cultural calibration
+    if region == 'east_asia':
+        # 매 East Asian 의 less expressive baseline
+        base_pred['happy'] *= 0.8
+        base_pred['neutral'] *= 1.2
+    return normalize(base_pred)
+```
+
+### LLM-based emotion (modern)
+```python
+def llm_emotion(text):
+    prompt = f"""Classify the emotion in this text. Output JSON.
+Text: "{text}"
+Output: {{"primary": "...", "secondary": "...", "valence": -1 to 1, "arousal": 0 to 1}}"""
+    return json.loads(llm.generate(prompt))
+```
+
+### Real-time engagement (online learning)
+```python
+class EngagementTracker:
+    def __init__(self, baseline=0.5):
+        self.engagement = baseline
+        self.history = []
+    
+    def update(self, frame):
+        emotion = analyze_face(frame)
+        self.engagement = 0.9 * self.engagement + 0.1 * emotion['attention_score']
+        self.history.append(self.engagement)
+    
+    def is_disengaged(self):
+        return np.mean(self.history[-30:]) < 0.3
+```
+
+### Affective response (chatbot)
+```python
+def empathic_response(user_msg):
+    emotion = classify(user_msg)
+    if emotion in ('sad', 'fear'):
+        return llm.generate(f"User feels {emotion}. Respond with validation first, then gentle reframe.\nUser: {user_msg}")
+    return llm.generate(f"Respond to: {user_msg}")
+```
+
+### Privacy-aware (on-device)
+```python
+# 매 raw frame 의 server 의 send X
+def on_device_emotion(frame, local_model):
+    emotion = local_model(frame)
+    # 매 only summary 의 send
+    return {'emotion': emotion, 'confidence': ...}
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Customer support text | Sentiment + LLM |
+| Driver drowsiness | Facial + on-device |
+| Mental health | Multimodal + clinician |
+| Marketing | A/B + reaction |
+| Robot pet | Multimodal real-time |
+| Education | Engagement (eye + face) |
+
+**기본값**: 매 task-specific modality + 매 cultural calibration + 매 privacy on-device + 매 LLM augment + 매 ethical disclosure.
+
+## 🔗 Graph
+- 부모: [[AI]] · [[Human Computer Interaction]]
+- 변형: [[Sentiment-Analysis]] · [[FER]] · [[Emotional-AI (Affective Computing)|Empathy-in-AI]]
+- Adjacent: [[Multimodal-LLM]] · [[Drama Management Systems]] · [[Dynamic Difficulty Adjustment (DDA)]] · [[EU-AI-Act]]
+
+## 🤖 LLM 활용
+**언제**: 매 customer experience. 매 health (with clinician). 매 robot interaction.
+**언제 X**: 매 surveillance. 매 manipulation. 매 EU high-risk.
+
+## ❌ 안티패턴
+- **Single-modality trust**: 매 noisy.
+- **Universal Ekman 의 assume**: 매 cultural bias.
+- **Express ≠ feel**: 매 mask.
+- **No consent**: 매 surveillance.
+- **Manipulate emotion**: 매 dark pattern.
+
+## 🧪 검증 / 중복
+- Verified (Picard 1997, Russell circumplex, Barrett constructionist).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-04-26 | EMOTION-AI auto |
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — emotion model + 매 text/voice/face/VA/multimodal code |