2nd/10_Wiki/Topics/AI_and_ML/Artificial-Life.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-artificial-life	Artificial Life (ALife)	10_Wiki/Topics	verified	self	ALife 인공 생명 digital evolution emergent behavior swarm intelligence	none	B	0.85	applied	alife evolutionary-computation emergence swarm multi-agent cellular-automata complexity simulation		2026-05-10	pending	language framework Python / C++ NEAT / DEAP / mesa / NetLogo

Artificial Life (ALife)

📌 한 줄 통찰

"매 life 의 본질 의 digital code 의 recreate". 매 simple rule 의 interaction → 매 emergent intelligence. 매 swarm AI / NPC behavior / evolutionary algorithm / open-ended learning 의 foundation.

📖 핵심

매 3 분류 (Langton)

1. Soft ALife: 매 software simulation. 매 Conway's Life, 매 Tierra, 매 Avida.
2. Hard ALife: 매 robot. 매 BEAM robotics, 매 swarm robot.
3. Wet ALife: 매 synthetic biology. 매 protocell, 매 artificial chemistry.

매 핵심 concept

1. Emergence: 매 simple rule → 매 complex pattern. (vs reductionism)
2. Self-organization: 매 central control X.
3. Adaptation: 매 environment 의 fit.
4. Reproduction: 매 self-replication (von Neumann).
5. Evolution: 매 mutation + selection.
6. Open-ended evolution: 매 stop X.

Landmark systems

• Conway's Game of Life (1970): 매 cellular automata.
• Tierra (Ray, 1991): 매 self-replicating program 의 evolution.
• Avida: 매 digital organism 의 lab.
• Karl Sims' Evolved Creatures (1994): 매 morphology + behavior 진화.
• NEAT (Stanley): 매 neural network 의 evolve.
• POET / Open-Ended ALife: 매 무한 challenge generation.

Boids (Reynolds 1987)

• 매 simple 3 rule:
  1. Separation: 매 collision 회피.
  2. Alignment: 매 neighbor 의 average heading.
  3. Cohesion: 매 neighbor 의 center.
• → 매 flocking / schooling / swarm.

Multi-agent emergence

• 매 ant colony 의 pheromone trail.
• 매 stigmergy: 매 environment 의 indirect communication.
• 매 termite mound 의 collective construction.

Evolutionary computation

• Genetic Algorithm (GA): 매 chromosome + crossover + mutation.
• Genetic Programming (GP): 매 program tree 의 evolve.
• Neuroevolution (NEAT, HyperNEAT): 매 NN 의 evolve.
• Evolution Strategy (ES, CMA-ES): 매 continuous parameter.
• Quality-Diversity (MAP-Elites, Novelty Search): 매 diversity 의 explicit.

매 modern AI 의 응용

1. NPC behavior (game): 매 boids 기반 swarm enemy.
2. Robotics: 매 swarm robot, 매 self-assembly.
3. Open-ended ML: 매 POET, 매 OMNI 의 curriculum.
4. Procedural generation: 매 cellular automata (cave, dungeon).
5. Drug discovery: 매 evolutionary search.
6. Architecture / design: 매 evolutionary design.

💻 패턴

Boids (flocking)

import numpy as np

def boid_step(positions, velocities, perception=10, sep=2):
    for i in range(len(positions)):
        neighbors = [j for j in range(len(positions))
                     if i != j and np.linalg.norm(positions[i]-positions[j]) < perception]
        if not neighbors: continue
        
        # 매 alignment
        align = np.mean([velocities[j] for j in neighbors], axis=0) - velocities[i]
        # 매 cohesion
        cohesion = np.mean([positions[j] for j in neighbors], axis=0) - positions[i]
        # 매 separation
        separation = sum((positions[i]-positions[j])
                         / np.linalg.norm(positions[i]-positions[j])**2
                         for j in neighbors
                         if np.linalg.norm(positions[i]-positions[j]) < sep)
        
        velocities[i] += 0.05*align + 0.01*cohesion + 0.1*separation
        positions[i] += velocities[i]
    return positions, velocities

Genetic Algorithm (DEAP)

from deap import base, creator, tools, algorithms
import random

creator.create('FitnessMax', base.Fitness, weights=(1.0,))
creator.create('Individual', list, fitness=creator.FitnessMax)

toolbox = base.Toolbox()
toolbox.register('attr_bool', random.randint, 0, 1)
toolbox.register('individual', tools.initRepeat, creator.Individual, toolbox.attr_bool, 100)
toolbox.register('population', tools.initRepeat, list, toolbox.individual)

def fitness(ind): return (sum(ind),)
toolbox.register('evaluate', fitness)
toolbox.register('mate', tools.cxTwoPoint)
toolbox.register('mutate', tools.mutFlipBit, indpb=0.05)
toolbox.register('select', tools.selTournament, tournsize=3)

pop = toolbox.population(n=300)
algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=40)

Conway's Game of Life

import numpy as np
from scipy.signal import convolve2d

def step(grid):
    K = np.array([[1,1,1],[1,0,1],[1,1,1]])
    n = convolve2d(grid, K, mode='same', boundary='wrap')
    return ((n == 3) | ((grid == 1) & (n == 2))).astype(int)

MAP-Elites (Quality-Diversity)

def map_elites(grid_size, generations=1000):
    archive = {}  # 매 (behavior_descriptor) → best (fitness, genome)
    
    for gen in range(generations):
        if not archive: 
            genome = random_genome()
        else:
            parent = random.choice(list(archive.values()))
            genome = mutate(parent[1])
        
        fitness, descriptor = evaluate(genome)
        cell = discretize(descriptor, grid_size)
        if cell not in archive or archive[cell][0] < fitness:
            archive[cell] = (fitness, genome)
    
    return archive

→ 매 single best X — 매 diverse 의 set.

🤔 결정 기준

문제	Tool
Game NPC swarm	Boids
Optimization (discrete)	GA / GP
NN architecture	NEAT
Continuous param	CMA-ES
Diversity 필요	MAP-Elites / Novelty
Procedural map	Cellular automata
Multi-agent emergence	NetLogo / mesa

기본값: 매 specific objective = GA. 매 diversity = MAP-Elites. 매 NN = NEAT or RL.

🔗 Graph

• 부모: Complexity-Theory · Emergence · Multi-Agent-Systems
• 변형: Cellular-Automata · Evolutionary-Computation · Swarm-Intelligence · Boids
• 응용: NEAT · Quality-Diversity · Procedural-Generation · Game-AI
• Adjacent: Reinforcement-Learning · Self-Organization · Stigmergy · Algorithmic-Biology

🤖 LLM 활용

언제: 매 NPC swarm design. 매 procedural generation. 매 evolutionary optimization. 매 emergent behavior research. 언제 X: 매 supervised learning 의 substitute. 매 explainability 가 필수.

❌ 안티패턴

• GA 의 small population: 매 premature convergence.
• No diversity preservation: 매 monoculture.
• Boids 의 ignore neighbor distance: 매 unrealistic flock.
• Evolution 의 short generation: 매 emergence X.
• Wet ALife 의 ethics 무시: 매 synthetic biology biosecurity.

🧪 검증 / 중복

• Verified (Langton, Reynolds, Sims, Stanley).
• 신뢰도 B.
• Related: Cellular-Automata · Evolutionary-Computation · Swarm-Intelligence.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — Langton 분류 + Boids + GA + MAP-Elites code