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-bayesian-brain-hypothesis
+title: Bayesian Brain Hypothesis
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Bayesian brain, predictive coding, free energy principle, active inference, Friston]
+duplicate_of: none
+source_trust_level: B
+confidence_score: 0.85
+verification_status: conceptual
+tags: [neuroscience, predictive-coding, bayesian, free-energy, friston, active-inference, perception, generative-model]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: neuroscience / cognitive science
+  applicable_to: [Active Inference Agents, Perception Models, World Models]
+---
+
+# Bayesian Brain Hypothesis
+
+## 📌 한 줄 통찰
+> **"매 brain = 매 inference engine"**. 매 incomplete sensor + 매 prior → 매 best guess (posterior). 매 Friston 의 Free Energy Principle 의 unify perception / action / learning. 매 modern world model + active inference 의 theoretical base.
+
+## 📖 핵심
+
+### 매 core claim
+- 매 brain = 매 generative model.
+- 매 perception = 매 Bayesian inference.
+- 매 prior + likelihood → posterior.
+- 매 surprise (prediction error) 의 minimize.
+
+### Bayes' theorem (perception version)
+$$P(\text{cause} | \text{sensation}) = \frac{P(\text{sensation} | \text{cause}) \cdot P(\text{cause})}{P(\text{sensation})}$$
+
+### 매 evidence
+1. **Optical illusion**: 매 prior 의 dominate.
+2. **Multisensory integration**: 매 weighted by reliability.
+3. **Cocktail party**: 매 prior context 의 segregate.
+4. **Phantom limb**: 매 prior 의 mismatch.
+5. **Schizophrenia**: 매 prior weighting 의 broken.
+
+### 매 핵심 개념
+
+#### Predictive Coding
+- 매 cortex 의 hierarchical prediction.
+- 매 top-down prediction + bottom-up error.
+- 매 error 만 의 propagate.
+- 매 efficient (most signal 의 cancelled).
+
+#### Free Energy Principle (Friston)
+- 매 organism 의 environment 의 surprise 의 minimize.
+- 매 free energy = upper bound on surprise.
+- 매 perception (model 의 update) + action (world 의 change) 의 둘 다.
+
+#### Active Inference
+- 매 action = 매 prediction error 의 reduce 의 way.
+- 매 motor 의 proprioception 의 prediction.
+- 매 RL 의 reward 의 alternative.
+
+#### Markov Blanket
+- 매 system 의 외부 / 내부 의 boundary.
+- 매 Friston 의 ontological foundation.
+
+### 매 layer (cortical)
+- 매 deep layer (5/6): 매 prediction (top-down).
+- 매 superficial (2/3): 매 error (bottom-up).
+- 매 NMDA / AMPA receptor 의 different role.
+
+### 매 modern AI 의 응용
+1. **World models** (Ha & Schmidhuber): 매 generative model 학습.
+2. **Active inference agent**: 매 RL 의 alternative.
+3. **PILCO / Dreamer**: 매 model-based RL.
+4. **Variational autoencoder** (VAE): 매 generative + recognition.
+5. **Predictive coding networks** (PredNet, Lotter): 매 NN 구현.
+6. **Self-supervised learning**: 매 prediction-based.
+
+### 매 disorder 의 explanation
+- **Autism**: 매 high-precision prior (less plasticity).
+- **Schizophrenia**: 매 low-precision prior + high error.
+- **Anxiety**: 매 over-prediction of negative.
+- **Depression**: 매 prior 의 negative bias.
+
+### 매 critique
+- **Falsifiability**: 매 거의 모든 것의 explain.
+- **Computational tractability**: 매 brain 의 actual implementation.
+- **Strong vs weak**: 매 metaphor vs 매 literal.
+
+## 💻 패턴 (응용 — active inference / predictive coding)
+
+### Predictive coding network
+```python
+import torch
+import torch.nn as nn
+
+class PredictiveCodingLayer(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.predictor = nn.Linear(dim, dim)  # 매 top-down
+    
+    def forward(self, top_down, bottom_up):
+        prediction = self.predictor(top_down)
+        error = bottom_up - prediction
+        # 매 error 만 의 propagate up
+        return error, prediction
+
+class PredNet(nn.Module):
+    def __init__(self, dims):
+        super().__init__()
+        self.layers = nn.ModuleList([PredictiveCodingLayer(d) for d in dims])
+    
+    def forward(self, x):
+        # 매 hierarchical prediction + error propagation
+        ...
+```
+
+### Active inference (mountain car)
+```python
+def active_inference_agent(observations, prior_belief):
+    # 매 1. perception: state 의 infer
+    posterior = bayes_update(prior_belief, observations)
+    
+    # 매 2. action selection: 매 expected free energy 의 minimize
+    actions = enumerate_actions()
+    efe = []
+    for a in actions:
+        # 매 epistemic value (information gain)
+        info_gain = expected_kl(posterior_after(a), posterior)
+        # 매 pragmatic value (preferred outcome)
+        pragmatic = expected_log_prior(a)
+        efe.append(-info_gain - pragmatic)
+    
+    return actions[np.argmin(efe)]
+```
+
+→ 매 reward X — 매 prediction error / preference.
+
+### Variational free energy
+```python
+import torch.distributions as dist
+
+def free_energy(q_phi, p_theta, observations):
+    # F = E_q[log q] - E_q[log p(o, s)]
+    s = q_phi.rsample()
+    log_q = q_phi.log_prob(s)
+    log_p_obs = p_theta.likelihood(observations, s)
+    log_p_prior = p_theta.prior(s)
+    return log_q - log_p_obs - log_p_prior
+```
+
+### World model (Dreamer-like)
+```python
+class WorldModel(nn.Module):
+    def __init__(self):
+        self.encoder = Encoder()        # 매 obs → state
+        self.dynamics = RSSM()          # 매 state + action → next state
+        self.decoder = Decoder()        # 매 state → obs (reconstruction)
+        self.reward_pred = RewardHead()
+    
+    def imagine(self, state, policy, horizon):
+        states, rewards = [], []
+        for _ in range(horizon):
+            action = policy(state)
+            state = self.dynamics(state, action)
+            states.append(state)
+            rewards.append(self.reward_pred(state))
+        return states, rewards
+```
+
+## 🤔 결정 기준
+| 응용 | Approach |
+|---|---|
+| Perception model | Predictive coding |
+| RL agent (model-based) | Dreamer / world model |
+| Sparse reward | Active inference |
+| Generative + recognition | VAE |
+| Hierarchical sensory | PredNet |
+| Mental disorder modeling | Bayesian brain framework |
+
+**기본값**: 매 perception = predictive coding. 매 action = active inference (sparse reward) or RL (dense).
+
+## 🔗 Graph
+- 부모: [[Computational-Neuroscience-RL|Computational-Neuroscience]] · [[Bayesian Inference]]
+- 변형: [[Predictive-Coding]] · [[Free-Energy-Principle]] · [[Active-Inference]]
+- 응용: [[World-Model]] · [[VAE]]
+- 사상가: [[Karl-Friston]]
+- Adjacent: [[Reinforcement-Learning]] · [[Generative-Model]]
+
+## 🤖 LLM 활용
+**언제**: 매 active inference agent design. 매 world model. 매 perception system. 매 sparse-reward RL.
+**언제 X**: 매 specific neuroscience claim 의 substitute. 매 medical diagnosis.
+
+## ❌ 안티패턴
+- **"매 brain literal"**: 매 metaphor 의 over-claim.
+- **No precision weighting**: 매 prior / likelihood 의 same weight.
+- **Strong free energy 의 unfalsifiable**: 매 모든 것 explain.
+- **Active inference 의 reward 의 conflate**: 매 different objective.
+- **Hierarchical 의 ignore**: 매 single-layer 의 limit.
+
+## 🧪 검증 / 중복
+- Verified (Friston, Rao-Ballard, Knill-Pouget).
+- 신뢰도 B (active research).
+- Related: [[Predictive-Coding]] · [[Free-Energy-Principle]] · [[World-Model]] · [[Active-Inference]].
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — Friston FEP + predictive coding + active inference + world model code |