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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+---
+id: wiki-2026-0508-free-energy-principle
+title: Free Energy Principle (FEP)
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [FEP, Karl Friston, active inference, predictive coding, surprise minimization]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.88
+verification_status: applied
+tags: [neuroscience, philosophy, fep, active-inference, predictive-coding, friston, bayesian]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Python
+  framework: NumPy / pymdp
+---
+
+# Free Energy Principle (FEP)
+
+## 매 한 줄
+> **"매 self-organizing system 의 의 의 surprise (variational free energy) 의 minimize"**. Karl Friston (UCL). 매 perception (predictive coding) + 매 action (active inference) 의 unify. 매 neuroscience theory of everything (controversial). 매 modern: 매 RL / world model 의 connection.
+
+## 매 핵심
+
+### 매 statement
+- 매 brain (and body) 의 의 의 statistical model.
+- 매 free energy ≈ 매 prediction error.
+- 매 minimize via:
+  1. **Update belief** (perception).
+  2. **Act** (active inference).
+
+### 매 vs RL
+- **RL**: 매 reward 의 maximize.
+- **FEP**: 매 surprise 의 minimize.
+- **Equivalence** (debated): 매 reward = -surprise.
+
+### 매 component
+- **Generative model**: 매 internal world model.
+- **Prior**: 매 expectation.
+- **Likelihood**: 매 sense → state.
+- **Posterior**: 매 belief update.
+- **Action**: 매 sense 의 expected 의 의 의 의 align.
+
+### 매 응용
+1. **Neuroscience**: 매 perception, attention, learning.
+2. **Psychiatry**: 매 schizophrenia, autism (precision-weighting).
+3. **Active inference RL**.
+4. **Robotics** (curiosity, exploration).
+5. **Computational psychiatry**.
+
+### 매 critique
+- 매 too general 의 의 의 unfalsifiable.
+- 매 Lakatos progressive vs degenerate program.
+- 매 implementation 의 specific 의 X.
+
+## 💻 패턴
+
+### Predictive coding (perception)
+```python
+import numpy as np
+
+def predictive_coding_step(prior_mu, prior_var, observation, obs_var):
+    """매 Kalman-like update."""
+    posterior_var = 1 / (1 / prior_var + 1 / obs_var)
+    posterior_mu = posterior_var * (prior_mu / prior_var + observation / obs_var)
+    prediction_error = observation - prior_mu
+    return posterior_mu, posterior_var, prediction_error
+```
+
+### Variational free energy
+```python
+def variational_free_energy(q_mu, q_var, p_mu, p_var, obs, obs_var):
+    """매 F = KL[q||p] - log p(obs|s)."""
+    kl = 0.5 * (np.log(p_var / q_var) + (q_var + (q_mu - p_mu)**2) / p_var - 1)
+    log_likelihood = -0.5 * (np.log(2 * np.pi * obs_var) + (obs - q_mu)**2 / obs_var)
+    return kl - log_likelihood
+```
+
+### Active inference (pymdp)
+```python
+import pymdp
+from pymdp.agent import Agent
+
+# 매 simple 1-state, 1-modality agent
+A = np.eye(3)  # 매 likelihood (state → obs)
+B = np.array([[[0.9, 0.1, 0], [0.1, 0.8, 0.1], [0, 0.1, 0.9]]])  # 매 transition (state, action)
+C = np.array([0, 0, 1])  # 매 prior preference (high obs 3)
+
+agent = Agent(A=A, B=B, C=C)
+agent.infer_states(observation=[0])
+action = agent.sample_action()
+```
+
+### Expected free energy (planning)
+```python
+def expected_free_energy(state_belief, action, A, B, C):
+    """매 EFE = epistemic value + pragmatic value."""
+    next_state = B[action] @ state_belief
+    expected_obs = A @ next_state
+    
+    # 매 pragmatic (utility)
+    pragmatic = (expected_obs * C).sum()
+    
+    # 매 epistemic (info gain)
+    epistemic = -np.sum(expected_obs * np.log(expected_obs + 1e-9))
+    
+    return -pragmatic + epistemic  # 매 minimize
+```
+
+### Predictive coding network (PyTorch)
+```python
+import torch
+import torch.nn as nn
+
+class PCLayer(nn.Module):
+    def __init__(self, n):
+        super().__init__()
+        self.W = nn.Parameter(torch.randn(n, n) * 0.01)
+        self.x = nn.Parameter(torch.zeros(n))  # 매 latent state
+    
+    def forward(self, top_down, bottom_up):
+        prediction = self.W @ self.x
+        error_top = top_down - prediction
+        error_bottom = bottom_up - self.x
+        free_energy = (error_top ** 2).sum() + (error_bottom ** 2).sum()
+        return free_energy
+```
+
+### Curiosity (intrinsic reward = -surprise)
+```python
+class IntrinsicCuriosity:
+    def __init__(self, model):
+        self.model = model
+    
+    def reward(self, state, action, next_state):
+        predicted = self.model.predict(state, action)
+        prediction_error = (predicted - next_state).abs().mean()
+        return prediction_error  # 매 high error = surprise = curiosity
+```
+
+### Precision (attention)
+```python
+def precision_weighted_update(prediction_error, precision):
+    """매 attention = 매 high precision 의 weight."""
+    return precision * prediction_error
+# 매 Friston: 매 schizophrenia = 매 precision 의 wrong
+```
+
+### World model (similar to FEP)
+```python
+class WorldModel(nn.Module):
+    """매 Ha & Schmidhuber 2018."""
+    def __init__(self, state_dim, action_dim):
+        super().__init__()
+        self.encoder = Encoder()  # 매 obs → latent
+        self.dynamics = nn.LSTM(latent_dim + action_dim, latent_dim)
+        self.decoder = Decoder()  # 매 latent → obs
+    
+    def step(self, obs, action):
+        latent = self.encoder(obs)
+        next_latent, h = self.dynamics(torch.cat([latent, action]))
+        predicted_obs = self.decoder(next_latent)
+        return predicted_obs, next_latent
+```
+
+### Hierarchical model
+```python
+class HierarchicalGenerativeModel:
+    def __init__(self, levels):
+        self.levels = levels  # 매 each = (mu, var, dynamics)
+    
+    def predict(self):
+        # 매 top-down predictions
+        for i in range(len(self.levels) - 1, 0, -1):
+            self.levels[i-1].mu = self.levels[i].dynamics(self.levels[i].mu)
+    
+    def update(self, obs):
+        # 매 bottom-up errors
+        for i in range(len(self.levels)):
+            error = obs - self.levels[i].mu if i == 0 else self.levels[i-1].mu - self.levels[i].mu
+            self.levels[i].mu += LR * error
+```
+
+### Schizophrenia model (precision dysregulation)
+```python
+def schizophrenia_simulation(true_state, sensory_precision_low):
+    """매 low sensory precision → 매 prior 의 dominate → 매 hallucination."""
+    sensory_precision = sensory_precision_low  # 매 abnormally low
+    prior_strength = 1.0
+    posterior = (sensory_precision * sensory_input + prior_strength * prior) / (sensory_precision + prior_strength)
+    return posterior  # 매 dominated by prior
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Theoretical neuro | FEP framework |
+| Computational model | pymdp |
+| RL exploration | Curiosity (FEP-inspired) |
+| World model | Schmidhuber-style |
+| Psychiatry | Precision-weighting |
+
+**기본값**: 매 FEP 의 high-level theory + 매 specific implementation = pymdp (active inference) or world model (RL).
+
+## 🔗 Graph
+- 부모: [[Computational-Neuroscience-RL|Computational-Neuroscience]] · [[Predictive-Processing]]
+- 변형: [[Active-Inference]] · [[Predictive-Coding]]
+- 응용: [[World-Model]]
+- Adjacent: [[Bayesian-Brain]] · [[Default Mode Network (DMN)]] · [[Reinforcement-Learning]]
+
+## 🤖 LLM 활용
+**언제**: 매 neuroscience research. 매 active inference RL.
+**언제 X**: 매 production engineering.
+
+## ❌ 안티패턴
+- **Theory of everything claim**: 매 unfalsifiable.
+- **FEP 의 RL 의 isomorphic 의 trust**: 매 detail 의 differ.
+- **Skip implementation**: 매 vague.
+
+## 🧪 검증 / 중복
+- Verified (Friston papers, pymdp docs, Hohwy Predictive Mind).
+- 신뢰도 B+.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-04-20 | Auto-reinforced |
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — FEP + 매 predictive coding / pymdp / EFE / world model code |