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-procedural-narrative-generation
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title: Procedural Narrative Generation
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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: [PNG, Story Generation, Computational Narrative, Drama Management]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.85
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verification_status: applied
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tags: [narrative, generative-ai, games, llm-applications]
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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 / TypeScript
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framework: LLM (Claude/GPT) + planner / state machine
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---
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# Procedural Narrative Generation
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## 매 한 줄
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> **"매 algorithmic story authoring — symbolic planners 부터 LLM-driven emergent narrative 까지"**. 1970s TaleSpin / 1990s drama managers (Façade) 의 lineage; 2019 AI Dungeon 으로 LLM era 시작, 2024-2026 currently agent-based simulation (Smallville, Voyager, Genesis) 에서 narrative emerges from LLM agents interacting in worlds. Hybrid 가 winning: LLM creativity + symbolic constraints.
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## 매 핵심
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### 매 paradigm timeline
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- **Story grammars (1970s-80s)**: Propp's morphology, Rumelhart, TaleSpin (Meehan 1977).
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- **Planning-based (1990s-2010s)**: STRIPS planners author plot (IPOCL, MEXICA).
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- **Drama management (2000s)**: Façade (Mateas & Stern 2005) reactive narrative.
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- **Neural (2017-2020)**: GPT-2/3 generated stories, Plug-and-Play LMs.
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- **AI Dungeon era (2019-)**: open-ended LLM-driven IF.
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- **Agent simulation (2023-2026)**: Park's Smallville, Voyager, Genesis world model — believable agents whose interactions form narrative.
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### 매 challenges
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1. **Coherence**: long-range plot consistency (LLMs forget).
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2. **Causality**: events must causally connect.
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3. **Character agency**: characters with goals/personalities, not just templates.
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4. **Author's vs character's intent**: meta-level narrative goals vs in-world goals.
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5. **Tellability**: not every simulation is a good story.
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### 매 modern hybrid stack
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- **World model**: structured state (locations, items, NPCs, relationships).
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- **LLM as narrator**: takes state + recent events → next beat.
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- **Symbolic planner**: enforces causal constraints, beats arc.
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- **Memory / retrieval**: vector DB of past events for consistency.
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- **Drama manager**: monitors arc (rising/falling tension), nudges plot.
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## 💻 패턴
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### LLM beat generator with state
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```python
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def generate_beat(state, history, beat_target):
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prompt = f"""You are a narrator. Current state:
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Characters: {state.characters}
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Location: {state.location}
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Inventory: {state.inventory}
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Recent events: {history[-5:]}
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Beat target: {beat_target} # e.g., "rising tension", "reveal villain"
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Generate the next narrative beat (2-3 sentences) and any state changes as JSON."""
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resp = claude.messages.create(
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model="claude-opus-4-7",
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max_tokens=400,
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messages=[{"role": "user", "content": prompt}],
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)
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return parse(resp.content[0].text)
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```
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### Story-arc drama manager (Freytag)
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```python
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ARC = ["exposition", "rising_action", "climax", "falling_action", "resolution"]
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class DramaManager:
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def __init__(self, n_beats=20):
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# Map beat index to arc stage
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self.targets = ([ARC[0]] * 3 + [ARC[1]] * 8 + [ARC[2]] * 2
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+ [ARC[3]] * 4 + [ARC[4]] * 3)
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def next_target(self, beat_idx, tension_so_far):
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target = self.targets[beat_idx]
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if target == "rising_action" and tension_so_far < 0.3:
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return "rising_action_inject_conflict"
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return target
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```
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### Goal-based character agent
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```python
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class CharacterAgent:
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def __init__(self, name, traits, goals):
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self.name, self.traits, self.goals = name, traits, goals
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self.memory = [] # recent observations
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def choose_action(self, world_state):
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# LLM call: given personality + memory + state, pick action
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prompt = f"""You are {self.name}. Traits: {self.traits}.
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Active goals: {self.goals}.
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Recent: {self.memory[-10:]}.
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World: {world_state}.
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What do you do next? Output: action(target, args)."""
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return llm(prompt)
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```
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### Causal consistency check
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```python
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def verify_beat(beat, state):
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"""Reject beats that violate world rules."""
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issues = []
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for change in beat.state_changes:
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if change.kind == "use_item" and change.item not in state.inventory:
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issues.append(f"{change.item} not in inventory")
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if change.kind == "location" and change.to not in state.adjacent_locs:
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issues.append(f"non-adjacent move {state.location}->{change.to}")
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return issues # caller re-prompts on issues
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```
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### Memory-augmented retrieval (Smallville-style)
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```python
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def retrieve_relevant(query, memory_db, k=5):
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"""Score = recency + importance + relevance (cosine)."""
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now = time.time()
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scored = []
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for m in memory_db:
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recency = 0.99 ** ((now - m.t) / 3600)
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relevance = cosine(embed(query), m.embedding)
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score = recency + m.importance + relevance
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scored.append((score, m))
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return [m for _, m in sorted(scored, reverse=True)[:k]]
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```
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### Branching narrative (interactive fiction)
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```python
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def turn(state, player_input):
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candidates = generate_n_continuations(state, player_input, n=3)
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chosen = drama_manager.pick(candidates, state.arc_position)
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state = apply(state, chosen)
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state.history.append(chosen)
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return state, chosen.text
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```
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### Constrained generation w/ JSON schema
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```python
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schema = {"type": "object", "properties": {
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"narration": {"type": "string"},
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"state_changes": {"type": "array", "items": {...}},
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"tension_delta": {"type": "number", "minimum": -1, "maximum": 1},
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}, "required": ["narration", "state_changes"]}
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# Use response_format=json_schema with Claude / GPT-5
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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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| Short interactive story | Pure LLM w/ state in prompt |
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| Long campaign (RPG) | LLM + structured world DB + retrieval |
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| Strong authorial intent | Planner + LLM surface text |
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| Emergent simulation | Agent-based (Smallville pattern) |
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| Branching narrative game | Drama manager + tree-of-beats |
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**기본값**: LLM beat-generator + structured world state + drama manager + retrieval memory.
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## 🔗 Graph
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- 부모: [[Computational-Narrative]] · [[Generative-AI]]
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- 변형: [[Interactive-Fiction]] · [[Drama-Management]]
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- Adjacent: [[Large-Language-Models]] · [[Planning]]
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## 🤖 LLM 활용
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**언제**: games (RPG, IF), creative writing tools, simulation, training data for narrative tasks.
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**언제 X**: factual reporting, deterministic content (ads/legal), regulated medical/legal narration.
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## ❌ 안티패턴
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- **Pure LLM, no state**: forgets at chapter 3, introduces dead characters.
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- **Planner-only, no LLM**: prose feels mechanical.
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- **No drama curve**: flat tension → boring.
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- **Unbounded creativity**: LLM invents items/locations with no consistency check.
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- **Single-shot 100k-token story**: better as iterative beats with state.
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## 🧪 검증 / 중복
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- Verified (Park et al 2023 Generative Agents UIST, Mateas & Stern 2005, Riedl & Bulitko 2013 survey).
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- 신뢰도 A (theory) / B (rapidly evolving practice).
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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 — full PNG history + 2026 hybrid LLM/agent stack |
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