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-rule-based-systems
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title: Rule-based Systems
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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: [Rule-based AI, Expert Systems, Production Systems]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [rule-based, expert-systems, symbolic-ai, neuro-symbolic, knowledge]
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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: Python
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framework: CLIPS / Drools / experta
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---
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# Rule-based Systems
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## 매 한 줄
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> **"매 IF condition THEN action 의 deterministic, auditable knowledge encoding"**. 매 1970s expert systems (MYCIN, DENDRAL, R1/XCON) 의 황금기 → 매 ML winter 후 무대 후퇴 → 매 2024+ neuro-symbolic 부활 (LLM + symbolic constraints, 매 hallucination 통제). 매 finance, healthcare, compliance, fraud 등 매 explainability 가 비협상 의 도메인 의 backbone.
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## 매 핵심
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### 매 Architecture
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- **Working Memory**: facts (current state).
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- **Rule Base**: production rules (LHS → RHS).
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- **Inference Engine**: forward chaining (data-driven) / backward chaining (goal-driven).
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- **Conflict resolution**: salience, recency, specificity.
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- **Rete algorithm**: efficient pattern matching (Forgy, 1982).
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### 매 Forms
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- **Production rules**: CLIPS, Drools, Jess.
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- **Decision tables**: tabular form, easy SME edit.
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- **Decision trees**: hierarchical, ML-extractable.
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- **Logic programming**: Prolog, Datalog, ASP.
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- **Business Rule Engines**: Camunda DMN, IBM ODM.
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### 매 응용
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1. Insurance underwriting / claims adjudication.
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2. Loan approval, AML/KYC, sanctions screening.
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3. Clinical decision support (drug interactions).
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4. Tax/compliance engines.
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5. Game AI (NPCs, behavior trees as rule variants).
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## 💻 패턴
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### CLIPS-style rule (experta)
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```python
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from experta import KnowledgeEngine, Rule, Fact, Field
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class Patient(Fact):
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fever = Field(bool, mandatory=True)
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cough = Field(bool, mandatory=True)
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duration_days = Field(int)
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class TriageEngine(KnowledgeEngine):
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@Rule(Patient(fever=True, cough=True, duration_days=lambda d: d >= 7))
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def likely_pneumonia(self):
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self.declare(Fact(diagnosis="pneumonia_suspect", urgent=True))
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@Rule(Patient(fever=True, duration_days=lambda d: d < 3))
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def viral(self):
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self.declare(Fact(diagnosis="viral_likely"))
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eng = TriageEngine()
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eng.reset()
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eng.declare(Patient(fever=True, cough=True, duration_days=8))
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eng.run()
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```
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### Drools rule (DRL syntax)
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```java
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rule "High value claim escalate"
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when
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$c: Claim(amount > 50000, status == "submitted")
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$p: Policy(id == $c.policyId, riskTier == "HIGH")
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then
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$c.setStatus("escalated_review");
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update($c);
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insert(new Alert("manual_review", $c.getId()));
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end
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```
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### DMN decision table (Camunda)
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```xml
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<decision id="discount" name="Customer Discount">
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<decisionTable hitPolicy="UNIQUE">
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<input label="Years"><inputExpression typeRef="number">years</inputExpression></input>
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<input label="Tier"><inputExpression typeRef="string">tier</inputExpression></input>
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<output label="Discount" typeRef="number"/>
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<rule><inputEntry><1</inputEntry><inputEntry>"BRONZE"</inputEntry><outputEntry>0</outputEntry></rule>
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<rule><inputEntry>[1..5]</inputEntry><inputEntry>"SILVER"</inputEntry><outputEntry>0.05</outputEntry></rule>
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<rule><inputEntry>>5</inputEntry><inputEntry>"GOLD"</inputEntry><outputEntry>0.15</outputEntry></rule>
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</decisionTable>
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</decision>
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```
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### Prolog (backward chaining)
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```prolog
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ancestor(X, Y) :- parent(X, Y).
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ancestor(X, Y) :- parent(X, Z), ancestor(Z, Y).
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parent(tom, bob).
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parent(bob, alice).
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% ?- ancestor(tom, alice). → true
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```
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### Neuro-symbolic — LLM proposes, rules verify (2026)
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```python
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import anthropic, json
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from rule_engine import compile_rules, evaluate
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client = anthropic.Anthropic()
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COMPLIANCE_RULES = compile_rules([
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"transaction.amount < 10000 OR transaction.kyc_verified == True",
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"transaction.country NOT IN sanctions_list",
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])
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resp = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=512,
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messages=[{"role": "user", "content": f"Extract transaction: {raw_text}"}],
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)
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tx = json.loads(resp.content[0].text)
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violations = evaluate(COMPLIANCE_RULES, tx)
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if violations:
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block(tx, reasons=violations) # rules give auditable why
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```
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### Forward-chaining engine in plain Python
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```python
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def forward_chain(rules, facts, max_iter=100):
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facts = set(facts)
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for _ in range(max_iter):
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new = set()
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for premises, conclusion in rules:
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if all(p in facts for p in premises) and conclusion not in facts:
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new.add(conclusion)
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if not new:
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break
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facts |= new
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return facts
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rules = [
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({"has_wings", "lays_eggs"}, "is_bird"),
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({"is_bird", "swims"}, "is_penguin"),
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]
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print(forward_chain(rules, {"has_wings", "lays_eggs", "swims"}))
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# {'has_wings', 'lays_eggs', 'swims', 'is_bird', 'is_penguin'}
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```
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### Behavior tree (game AI rule variant)
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```python
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from py_trees import behaviours, composites, common
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root = composites.Selector("Root", memory=False)
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attack = composites.Sequence("Attack", memory=True, children=[
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behaviours.Condition("EnemyVisible", "EnemyVisible", common.Status.SUCCESS),
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behaviours.Dummy("FireWeapon"),
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])
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patrol = behaviours.Dummy("Patrol")
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root.add_children([attack, patrol])
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Auditable, regulated | Rule engine (Drools, DMN) |
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| Pattern recognition | ML / DL |
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| Hybrid (extract + decide) | LLM + rule verifier (neuro-symbolic) |
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| Few thousand cases, frequent change | Decision table |
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| Complex ontology | Prolog / Datalog / ASP |
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**기본값**: Drools/DMN for business rules, LLM-extract + rule-verify for unstructured input.
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## 🔗 Graph
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- 부모: [[Symbolic-AI]] · [[Knowledge-Representation]]
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- 변형: [[Expert-Systems]]
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- Adjacent: [[Neural-Symbolic-Integration|Neuro-Symbolic-AI]]
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## 🤖 LLM 활용
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**언제**: rule extraction from policy docs, NL-to-DRL translation, rule conflict detection, explanation generation.
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**언제 X**: as the rule engine itself in regulated domains — use deterministic engine, let LLM only structure the input.
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## ❌ 안티패턴
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- **Rule explosion**: 5000+ rules without modules → unmaintainable; refactor to decision tables.
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- **Conflicting rules silent**: no conflict detection → undefined behavior.
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- **No version control**: rules edited live in production BRMS UI without git.
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- **LLM as judge for compliance**: hallucination in regulated path; LLM must propose, rules must verify.
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## 🧪 검증 / 중복
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- Verified (Russell & Norvig AIMA, CLIPS docs, Drools manual, OMG DMN spec).
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- 신뢰도 A.
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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 — production rules, Rete, neuro-symbolic 2026 |
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