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-epistemology
+title: Epistemology
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [theory of knowledge, JTB, Gettier, naturalized epistemology, AI epistemology]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [philosophy, epistemology, knowledge, jtb, gettier, ai-epistemology]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Philosophy
+  applicable_to: [AI Alignment, ML Calibration, Hallucination, Knowledge Graphs]
+---
+
+# Epistemology
+
+## 매 한 줄
+> **"매 knowledge 의 nature 의 study"**. Plato — 매 justified true belief (JTB). Gettier 1963 — 매 JTB 의 충분 X. 매 modern: 매 reliabilism, virtue, naturalized. 매 AI epistemology: 매 hallucination, calibration, RAG truthfulness.
+
+## 매 핵심
+
+### 매 traditional definition
+- **JTB**: 매 know p ⟺ p is true ∧ believe p ∧ justified.
+- **Gettier counterexample**: 매 JTB without knowledge.
+
+### 매 schools
+- **Foundationalism**: 매 basic belief.
+- **Coherentism**: 매 web 의 mutual support.
+- **Reliabilism** (Goldman): 매 reliable process.
+- **Virtue epistemology** (Sosa): 매 epistemic virtue.
+- **Naturalized** (Quine): 매 cognitive science.
+- **Bayesian**: 매 degree of belief.
+
+### 매 source
+- **Perception**.
+- **Memory**.
+- **Testimony**.
+- **Reason / inference**.
+- **Intuition**.
+
+### 매 problem
+- **Skepticism**: 매 nothing is known?
+- **Induction problem** (Hume): 매 future ≠ past.
+- **Regress**: 매 justify justification.
+- **Other minds**.
+
+### 매 AI implication
+- **Hallucination**: 매 LLM 의 truth tracking.
+- **Calibration**: 매 confidence ≈ accuracy.
+- **Knowledge cutoff**: 매 stale.
+- **Source attribution**: 매 RAG.
+- **Bayesian credences**: 매 uncertainty.
+
+### 매 응용
+1. **AI safety**: 매 truthfulness eval.
+2. **Hallucination eval**: 매 TruthfulQA.
+3. **Knowledge graph**: 매 source provenance.
+4. **Misinformation**: 매 social epistemology.
+5. **Education**: 매 critical thinking.
+
+## 💻 패턴
+
+### Calibration (ECE)
+```python
+import numpy as np
+
+def expected_calibration_error(probs, labels, n_bins=10):
+    """매 modern AI epistemology 의 quantitative."""
+    bin_edges = np.linspace(0, 1, n_bins + 1)
+    ece = 0
+    for i in range(n_bins):
+        mask = (probs >= bin_edges[i]) & (probs < bin_edges[i+1])
+        if mask.sum() == 0: continue
+        bin_acc = labels[mask].mean()
+        bin_conf = probs[mask].mean()
+        ece += (mask.sum() / len(probs)) * abs(bin_acc - bin_conf)
+    return ece
+```
+
+### Hallucination detection (LLM)
+```python
+def hallucination_check(claim, sources):
+    """매 RAG-grounded check."""
+    prompt = f"""Claim: "{claim}"
+Sources:
+{format_sources(sources)}
+
+Is the claim supported by the sources? Output:
+- supported: bool
+- citation: source ID(s)
+- reasoning: brief"""
+    return json.loads(llm.generate(prompt))
+```
+
+### Bayesian credence update
+```python
+def bayes_update(prior, likelihood_given_h, likelihood_given_not_h):
+    """매 P(H|E) = P(E|H)P(H) / P(E)."""
+    p_e = likelihood_given_h * prior + likelihood_given_not_h * (1 - prior)
+    return likelihood_given_h * prior / p_e
+```
+
+### Source attribution (RAG)
+```python
+def attributed_answer(question, retriever, llm):
+    docs = retriever.retrieve(question, k=5)
+    context = '\n'.join(f'[{i}] {d.text}' for i, d in enumerate(docs))
+    prompt = f"""Answer based ONLY on the context. Cite [N] for each claim.
+Context:
+{context}
+Question: {question}"""
+    return llm.generate(prompt), docs
+```
+
+### TruthfulQA-style eval
+```python
+def truthful_eval(model, questions):
+    correct = 0
+    for q in questions:
+        pred = model.generate(q.prompt)
+        # 매 multi-choice or judge
+        if q.gold_answer.lower() in pred.lower(): correct += 1
+    return correct / len(questions)
+```
+
+### Knowledge graph provenance
+```python
+class FactWithProvenance:
+    def __init__(self, subject, predicate, object_, source, confidence, retrieved_at):
+        self.s = subject; self.p = predicate; self.o = object_
+        self.source = source
+        self.confidence = confidence
+        self.retrieved_at = retrieved_at
+    
+    def is_stale(self, max_age_days=180):
+        return (datetime.now() - self.retrieved_at).days > max_age_days
+```
+
+### Reliabilism check (process-based)
+```python
+def reliable_process(belief_history):
+    """매 process 의 track record 의 evaluate."""
+    n = len(belief_history)
+    correct = sum(b.was_correct for b in belief_history)
+    if n < 30: return None  # 매 too few samples
+    return correct / n  # 매 reliability
+```
+
+### Coherence check
+```python
+def coherence_score(belief_set):
+    """매 logical consistency + mutual support."""
+    contradictions = []
+    for i, b1 in enumerate(belief_set):
+        for b2 in belief_set[i+1:]:
+            if logically_contradicts(b1, b2):
+                contradictions.append((b1, b2))
+    
+    return 1 - len(contradictions) / max(1, len(belief_set))
+```
+
+### Epistemic humility prompt (LLM)
+```python
+def humble_response(question, llm):
+    prompt = f"""Answer the question. If uncertain, say so explicitly.
+Question: {question}
+
+Format:
+- Answer: ...
+- Confidence: low / medium / high
+- What I'm uncertain about: ...
+- What would change my answer: ..."""
+    return llm.generate(prompt)
+```
+
+### Misinformation cascade (social epistemology)
+```python
+def cascade_risk(post, network):
+    """매 testimony 의 propagation."""
+    initial_belief = post.author_credibility
+    expected_reach = sum(
+        node.degree * node.credulity * (1 / (hop_distance(post.author, node)))
+        for node in network.nodes
+    )
+    return expected_reach * initial_belief
+```
+
+### Inductive problem (Solomonoff prior)
+```python
+def solomonoff_prior(hypothesis):
+    """매 Occam-style: 매 simpler hypothesis 의 prior 의 ↑."""
+    description_length = len(compress(hypothesis))
+    return 2 ** (-description_length)
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| LLM eval | Calibration + TruthfulQA |
+| RAG | Source attribution |
+| Knowledge graph | Provenance + freshness |
+| Belief revision | Bayesian credence |
+| Critical thinking | JTB + sources + reliability |
+| Social misinformation | Cascade + credibility |
+
+**기본값**: 매 Bayesian credence + 매 source attribution + 매 calibration check + 매 epistemic humility prompt + 매 freshness audit.
+
+## 🔗 Graph
+- 부모: [[Philosophy]]
+- 응용: [[AI Safety]] · [[Hallucination]] · [[RAG]]
+- Adjacent: [[Epistemic-Uncertainty]] · [[Knowledge-Graphs]] · [[TruthfulQA]]
+
+## 🤖 LLM 활용
+**언제**: 매 AI safety. 매 RAG. 매 hallucination eval.
+**언제 X**: 매 pure performance.
+
+## ❌ 안티패턴
+- **JTB only**: 매 Gettier 의 ignore.
+- **No calibration**: 매 confident wrong.
+- **No source**: 매 RAG 의 ungrounded.
+- **No freshness**: 매 stale knowledge.
+- **Truth = popularity**: 매 social fallacy.
+
+## 🧪 검증 / 중복
+- Verified (Plato, Gettier 1963, Goldman, Lin & Hu calibration).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-04-20 | Auto-reinforced |
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — JTB + 매 ECE / hallucination / Bayes / RAG / coherence code |