2nd/10_Wiki/Topics/AI_and_ML/Computational_Creativity.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-computational-creativity	Computational Creativity	10_Wiki/Topics	verified	self	computational creativity AI creativity generative AI Margaret Boden exploration vs exploitation	none	B	0.85	applied	creativity generative-ai computational-creativity boden novelty-search quality-diversity art		2026-05-10	pending	language applicable_to psychology / AI Generative AI Game Design Music Generation Co-creation Tools

Computational Creativity

매 한 줄

"매 machine 의 inspiration". Margaret Boden 의 3 type (combinational + exploratory + transformational). 매 modern: 매 GAN, 매 diffusion, 매 LLM, 매 quality-diversity, 매 evolutionary. 매 imitation 의 X — 매 transformational 의 frontier.

매 핵심

Margaret Boden's 3 type

1. Combinational: 매 existing concept 의 새 combination.
2. Exploratory: 매 concept space 의 frontier exploration.
3. Transformational: 매 space rule 의 break — 매 새 paradigm.

→ 매 LLM / Diffusion 의 most 의 #1 + #2. #3 의 still elusive.

매 evaluation criteria

• Novelty: 매 다른 가?
• Quality: 매 좋 가?
• Surprise (Bayesian): 매 expectation 의 violate?
• Value (utility): 매 use 가능?

→ 매 4 의 trade-off (novelty vs quality 의 typical).

매 method

Generative

• GAN: 매 photoreal.
• VAE: 매 latent control.
• Diffusion: 매 modern (Stable, Flux).
• LLM: 매 text, code.
• AutoEncoder.

Search

• Genetic algorithm.
• Simulated annealing.
• MCTS.
• Bayesian optimization.

Open-ended

• Novelty Search: 매 "새로움 만" objective.
• MAP-Elites: 매 quality-diversity.
• POET: 매 endless challenge.

Hybrid

• Human-AI co-creation.
• Critic + generator (GAN-like).

매 응용

Art

• Image: Stable Diffusion, Midjourney.
• Music: Suno, Udio, MusicGen.
• Video: Sora, Veo, Runway.
• 3D: DreamFusion.

Game

• PCG (Procedural Content Generation).
• Level design.
• NPC behavior.

Science

• AlphaFold: 매 protein design.
• Drug discovery.
• Material design (GNoME).

Engineering

• Topology optimization.
• Architecture (building) generation.

Code

• Copilot, Cursor: 매 code completion.
• Aider, Cline: 매 agentic generation.

매 philosophy debate

• "Can machines truly be creative?"
• 매 Strong vs Weak AI 의 connection.
• 매 Searle's Chinese Room.
• 매 attribution / authorship.

→ 매 Boden 의 implementation 가능, 매 transformational 의 hardest.

매 modern issue

• Authorship: 매 AI 가 / 매 prompt 의 user 가.
• Training data: 매 copyright (LAION lawsuit).
• Evaluation: 매 subjective.
• Mode collapse: 매 generic look.
• Slop: 매 quantity > quality.

💻 패턴

Novelty search (vs fitness-based)

import numpy as np

class NoveltySearch:
    def __init__(self, k=15):
        self.archive = []
        self.k = k
    
    def novelty(self, behavior):
        if not self.archive: return 1.0
        distances = [np.linalg.norm(behavior - b) for b in self.archive]
        return np.mean(sorted(distances)[:self.k])
    
    def add(self, behavior):
        if self.novelty(behavior) > 0.5:  # 매 threshold
            self.archive.append(behavior)
    
    def select(self, candidates):
        # 매 select most novel — 매 not most fit
        return max(candidates, key=lambda c: self.novelty(c.behavior))

MAP-Elites (quality-diversity)

from collections import defaultdict

def map_elites(generations=1000, mutate_fn=None, evaluate_fn=None, descriptor_fn=None, grid=10):
    archive = {}  # 매 cell → (fitness, genome)
    
    for gen in range(generations):
        if not archive:
            genome = random_genome()
        else:
            parent = random.choice(list(archive.values()))[1]
            genome = mutate_fn(parent)
        
        fitness = evaluate_fn(genome)
        descriptor = descriptor_fn(genome)
        cell = tuple(int(d * grid) for d in descriptor)
        
        if cell not in archive or archive[cell][0] < fitness:
            archive[cell] = (fitness, genome)
    
    return archive  # 매 grid 의 매 cell 의 best

Co-creation UI (LLM-aided)

function CoCreationCanvas({ prompt, onUpdate }) {
  return (
    <div>
      <textarea value={prompt} onChange={e => onUpdate(e.target.value)} />
      <div className="suggestions">
        {/* 매 LLM 의 매 alternative direction */}
        {alternatives.map(alt => (
          <button onClick={() => onUpdate(alt)}>
            {alt.description}
          </button>
        ))}
      </div>
      <div className="generated">
        {/* 매 다양한 stage 의 generated content */}
      </div>
    </div>
  );
}

Diffusion + style mixing

def style_combine(prompt_a, prompt_b, mix_ratio=0.5, n_steps=30):
    """매 두 prompt 의 latent 의 mix → 매 combinational creativity."""
    text_emb_a = text_encoder(prompt_a)
    text_emb_b = text_encoder(prompt_b)
    
    mixed = mix_ratio * text_emb_a + (1 - mix_ratio) * text_emb_b
    
    image = diffusion_pipe(prompt_embeds=mixed, num_inference_steps=n_steps).images[0]
    return image

Music generation (MusicGen)

from audiocraft.models import MusicGen

model = MusicGen.get_pretrained('facebook/musicgen-large')
model.set_generation_params(duration=30)

descriptions = ['hip hop with energetic drums', 'ambient piano with rain']
wav = model.generate(descriptions)

PCG (procedural game level)

def generate_level(theme, difficulty, seed=None):
    """매 cellular automata + 매 ML constraint."""
    rng = np.random.default_rng(seed)
    
    # 매 base CA
    grid = rng.choice([0, 1], size=(50, 50), p=[0.55, 0.45])
    for _ in range(5):
        grid = ca_smooth(grid)
    
    # 매 difficulty constraint
    if difficulty == 'hard':
        grid = add_obstacles(grid, density=0.1)
    
    # 매 theme 의 ML refine
    grid = theme_model(grid, theme)
    
    return grid

Critic-guided generation

def critic_guided(generator, critic, prompt, n_iters=5):
    """매 GAN-like — 매 generator + 매 critic 의 loop."""
    candidate = generator.generate(prompt)
    
    for i in range(n_iters):
        critique = critic.evaluate(candidate, criteria=['novelty', 'quality', 'on-prompt'])
        if all(c.score > 0.7 for c in critique): break
        candidate = generator.refine(prompt, candidate, critique)
    
    return candidate

POET (open-ended evolution)

class POET:
    """매 open-ended challenge generation + 매 agent evolution."""
    def __init__(self):
        self.environments = []
        self.agents = []
    
    def step(self):
        # 매 1. 매 mutate environment (challenge)
        new_env = mutate_env(random.choice(self.environments))
        if not too_easy(new_env) and not too_hard(new_env):
            self.environments.append(new_env)
        
        # 매 2. 매 mutate agent
        for env in self.environments:
            for agent in self.agents:
                if not agent.solved(env):
                    agent.train_on(env)
        
        # 매 3. 매 transfer (agent A 의 env B 의 try)
        for agent in self.agents:
            for env in self.environments:
                if agent.solves(env):
                    record_solver(agent, env)

Authorship / attribution

def attribute_authorship(work, generation_log):
    return {
        'human_contribution': {
            'prompts': generation_log.prompts,
            'curation_decisions': generation_log.selections,
            'manual_edits': generation_log.manual_edits,
        },
        'ai_contribution': {
            'model': generation_log.model_version,
            'generation_count': generation_log.total_generations,
            'auto_decisions': generation_log.auto_choices,
        },
        'attribution': 'human-led (prompt + curate + edit)' if 
            len(generation_log.manual_edits) > 5 else 'AI-generated, human-curated',
    }

🤔 결정 기준

목적	Approach
Image	Diffusion (SD, Flux, MJ)
Music	MusicGen / Suno
Code	LLM (Cursor, Copilot)
Game level	PCG + ML
Drug	AlphaFold + RFdiffusion
Open-ended	POET / MAP-Elites
Co-creation	LLM + human
Transformational	매 still hard

기본값: 매 task-specific generative + 매 human curation + 매 attribution.

🔗 Graph

• 부모: AI · Creativity · Generative-AI
• 응용: Stable-Diffusion · AlphaFold · PCG
• Adjacent: Authenticity · Arts · Artificial-Life · Beckett (creative AI metaphor)

🤖 LLM 활용

언제: 매 generative system design. 매 co-creation tool. 매 PCG. 매 art / music / writing. 언제 X: 매 deterministic task. 매 fact-based.

❌ 안티패턴

• Imitation 만: 매 transformational 의 X.
• Mode collapse: 매 single style.
• No human curate: 매 slop.
• No attribution: 매 ethics.
• Single objective (fitness only): 매 novelty 의 lose.
• Copyright 무시: 매 lawsuit risk.

🧪 검증 / 중복

• Verified (Boden "Creative Mind", Stanley novelty search, MAP-Elites, ALife conferences).
• 신뢰도 B.
• Related: Arts · Authenticity · Artificial-Life · Stable-Diffusion · Best-of-N_Sampling.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — Boden 3 + 매 novelty search / MAP-Elites / POET / co-creation code