"""
Neuro-Symbolic Autonomy Framework (NSAF) - Self-Constructing Meta-Agents
=======================================================================
Author: Bolorerdene Bundgaa
Contact: bolor@ariunbolor.org
Website: https://bolor.me
Self-Constructing Meta-Agents (SCMA) implementation with evolutionary
agent generation and distributed training capabilities.
"""
from typing import List, Dict, Any, Optional, Tuple
import numpy as np
from dataclasses import dataclass
import yaml
import logging
from pathlib import Path
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import ray
@dataclass
class AgentGenome:
"""Represents the genetic encoding of an agent's architecture and capabilities"""
id: str
architecture: Dict[str, Any] # Neural architecture parameters
capabilities: List[str] # List of agent capabilities
hyperparameters: Dict[str, Any] # Training hyperparameters
fitness: float = 0.0 # Agent's performance score
generation: int = 0 # Generation number
parent_ids: Optional[List[str]] = None # IDs of parent agents
class AgentPopulation(Dataset):
"""Dataset class for managing a population of agents"""
def __init__(self, genomes: List[AgentGenome]):
self.genomes = genomes
def __len__(self) -> int:
return len(self.genomes)
def __getitem__(self, idx: int) -> AgentGenome:
return self.genomes[idx]
@ray.remote
class SCMAWorker:
"""Distributed worker for agent evolution and training"""
def __init__(self, config: Dict[str, Any]):
self.config = config
def evaluate_agent(self, genome: AgentGenome) -> float:
"""Evaluate an agent's fitness"""
# TODO: Implement actual evaluation logic
return np.random.random() # Placeholder
def train_agent(self, genome: AgentGenome) -> Tuple[AgentGenome, float]:
"""Train an agent and return its updated genome and performance"""
# TODO: Implement actual training logic
return genome, np.random.random() # Placeholder
class SCMA:
"""
Self-Constructing Meta-Agents (SCMA) system for generating and optimizing AI agents
through evolutionary algorithms and neural architecture search.
"""
def __init__(self, config_path: str = "config/config.yaml"):
# Load configuration
with open(config_path, 'r') as f:
self.config = yaml.safe_load(f)['scma']
# Initialize parameters
self.population_size = self.config['generation']['population_size']
self.n_generations = self.config['generation']['generations']
self.mutation_rate = self.config['generation']['mutation_rate']
self.crossover_rate = self.config['generation']['crossover_rate']
# Setup logging
logging.basicConfig(level=logging.INFO)
self.logger = logging.getLogger(__name__)
# Initialize Ray for distributed computing
if not ray.is_initialized():
ray.init()
# Create worker pool
cpu_count = int(ray.available_resources().get('CPU', 4))
self.workers = [SCMAWorker.remote(self.config)
for _ in range(cpu_count)]
def _initialize_population(self) -> List[AgentGenome]:
"""Initialize a population of random agent genomes"""
population = []
for i in range(self.population_size):
# Generate random architecture
architecture = {
'n_layers': np.random.randint(2, 8),
'hidden_dims': [np.random.randint(32, 512)
for _ in range(np.random.randint(2, 8))],
'activation': np.random.choice(['relu', 'tanh', 'gelu']),
'attention_heads': np.random.randint(4, 16)
}
# Generate random capabilities
capabilities = np.random.choice(
['reasoning', 'perception', 'planning', 'learning'],
size=np.random.randint(1, 4),
replace=False
).tolist()
# Generate random hyperparameters
hyperparameters = {
'learning_rate': 10 ** np.random.uniform(-4, -2),
'batch_size': np.random.choice([16, 32, 64, 128]),
'optimizer': np.random.choice(['adam', 'sgd', 'adamw'])
}
genome = AgentGenome(
id=f"agent_{i}_gen_0",
architecture=architecture,
capabilities=capabilities,
hyperparameters=hyperparameters
)
population.append(genome)
return population
def _mutate(self, genome: AgentGenome) -> AgentGenome:
"""Apply mutation to an agent's genome"""
if np.random.random() < self.mutation_rate:
# Mutate architecture
if np.random.random() < 0.3:
genome.architecture['n_layers'] = max(2,
genome.architecture['n_layers'] + np.random.randint(-1, 2))
if np.random.random() < 0.3:
layer_idx = np.random.randint(len(genome.architecture['hidden_dims']))
genome.architecture['hidden_dims'][layer_idx] = max(32,
genome.architecture['hidden_dims'][layer_idx] +
np.random.randint(-64, 65))
if np.random.random() < 0.2:
genome.architecture['activation'] = np.random.choice(
['relu', 'tanh', 'gelu'])
if np.random.random() < 0.2:
genome.architecture['attention_heads'] = max(1,
genome.architecture['attention_heads'] + np.random.randint(-2, 3))
# Mutate capabilities
if np.random.random() < 0.2:
all_capabilities = ['reasoning', 'perception', 'planning', 'learning']
if np.random.random() < 0.5 and len(genome.capabilities) > 1:
# Remove a capability
genome.capabilities.remove(
np.random.choice(genome.capabilities))
else:
# Add a capability
available = list(set(all_capabilities) -
set(genome.capabilities))
if available:
genome.capabilities.append(np.random.choice(available))
# Mutate hyperparameters
if np.random.random() < 0.3:
genome.hyperparameters['learning_rate'] *= np.random.uniform(0.5, 2.0)
if np.random.random() < 0.2:
genome.hyperparameters['batch_size'] = np.random.choice(
[16, 32, 64, 128])
if np.random.random() < 0.1:
genome.hyperparameters['optimizer'] = np.random.choice(
['adam', 'sgd', 'adamw'])
return genome
def _crossover(self, parent1: AgentGenome, parent2: AgentGenome) -> AgentGenome:
"""Perform crossover between two parent genomes"""
if np.random.random() < self.crossover_rate:
# Crossover architecture
architecture = {
'n_layers': np.random.choice(
[parent1.architecture['n_layers'],
parent2.architecture['n_layers']]),
'hidden_dims': [],
'activation': np.random.choice(
[parent1.architecture['activation'],
parent2.architecture['activation']]),
'attention_heads': np.random.choice(
[parent1.architecture['attention_heads'],
parent2.architecture['attention_heads']])
}
# Crossover hidden dimensions
max_layers = min(len(parent1.architecture['hidden_dims']),
len(parent2.architecture['hidden_dims']))
for i in range(architecture['n_layers']):
if i < max_layers:
architecture['hidden_dims'].append(np.random.choice(
[parent1.architecture['hidden_dims'][i],
parent2.architecture['hidden_dims'][i]]))
else:
architecture['hidden_dims'].append(
np.random.randint(32, 512))
# Crossover capabilities
capabilities = list(set(parent1.capabilities + parent2.capabilities))
np.random.shuffle(capabilities)
capabilities = capabilities[:np.random.randint(1, len(capabilities) + 1)]
# Crossover hyperparameters
hyperparameters = {
'learning_rate': np.random.choice(
[parent1.hyperparameters['learning_rate'],
parent2.hyperparameters['learning_rate']]),
'batch_size': np.random.choice(
[parent1.hyperparameters['batch_size'],
parent2.hyperparameters['batch_size']]),
'optimizer': np.random.choice(
[parent1.hyperparameters['optimizer'],
parent2.hyperparameters['optimizer']])
}
child = AgentGenome(
id=f"agent_{np.random.randint(1000000)}_gen_{parent1.generation + 1}",
architecture=architecture,
capabilities=capabilities,
hyperparameters=hyperparameters,
generation=parent1.generation + 1,
parent_ids=[parent1.id, parent2.id]
)
return child
else:
# Return copy of random parent
return parent1 if np.random.random() < 0.5 else parent2
@ray.remote
def _evaluate_population(self, population: List[AgentGenome]) -> List[float]:
"""Evaluate fitness for all agents in parallel"""
future_fitness = []
for genome in population:
worker = np.random.choice(self.workers)
future_fitness.append(worker.evaluate_agent.remote(genome))
return ray.get(future_fitness)
def evolve_agents(self, task_requirements: Dict[str, Any]) -> List[AgentGenome]:
"""
Evolve a population of agents to meet task requirements.
Args:
task_requirements: Dictionary specifying required capabilities and constraints
Returns:
List[AgentGenome]: List of evolved agent genomes
"""
# Initialize population
population = self._initialize_population()
for generation in range(self.n_generations):
self.logger.info(f"Generation {generation + 1}/{self.n_generations}")
# Evaluate population
fitness_scores = ray.get(self._evaluate_population.remote(population))
for genome, fitness in zip(population, fitness_scores):
genome.fitness = fitness
# Sort population by fitness
population.sort(key=lambda x: x.fitness, reverse=True)
# Select top performers
n_elite = int(0.1 * self.population_size)
elite = population[:n_elite]
# Create new population through crossover and mutation
new_population = elite.copy() # Keep elite individuals
while len(new_population) < self.population_size:
# Select parents using tournament selection
tournament_size = 5
parent1 = max(np.random.choice(population, tournament_size),
key=lambda x: x.fitness)
parent2 = max(np.random.choice(population, tournament_size),
key=lambda x: x.fitness)
# Create child through crossover
child = self._crossover(parent1, parent2)
# Apply mutation
child = self._mutate(child)
new_population.append(child)
population = new_population
# Log progress
best_fitness = max(genome.fitness for genome in population)
avg_fitness = np.mean([genome.fitness for genome in population])
self.logger.info(f"Best fitness: {best_fitness:.4f}, "
f"Average fitness: {avg_fitness:.4f}")
return population
def train_agents(self, genomes: List[AgentGenome]) -> List[AgentGenome]:
"""Train evolved agents using distributed computing"""
future_results = []
for genome in genomes:
worker = np.random.choice(self.workers)
future_results.append(worker.train_agent.remote(genome))
results = ray.get(future_results)
trained_genomes = [result[0] for result in results]
return trained_genomes
def generate_optimal_agent(self, task_requirements: Dict[str, Any]) -> AgentGenome:
"""
Generate an optimal agent for given task requirements.
Args:
task_requirements: Dictionary specifying required capabilities and constraints
Returns:
AgentGenome: Optimal agent genome
"""
# Evolve population
population = self.evolve_agents(task_requirements)
# Select best agent
best_agent = max(population, key=lambda x: x.fitness)
# Train best agent
trained_agent = self.train_agents([best_agent])[0]
return trained_agent
