"""
Neuro-Symbolic Autonomy Framework (NSAF) - Task Clustering
==========================================================
Author: Bolorerdene Bundgaa
Contact: bolor@ariunbolor.org
Website: https://bolor.me
Implements quantum-enhanced symbolic task clustering for decomposing complex problems
into manageable task clusters.
"""
from typing import List, Dict, Any, Optional
import numpy as np
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit_aer import AerSimulator
from scipy.spatial.distance import cosine
import networkx as nx
from dataclasses import dataclass
import yaml
import logging
from pathlib import Path
@dataclass
class TaskCluster:
"""Represents a cluster of related tasks"""
id: str
tasks: List[Dict[str, Any]]
centroid: np.ndarray
similarity_score: float
quantum_state: Optional[np.ndarray] = None
class QuantumSymbolicTaskClustering:
"""
Implements quantum-enhanced symbolic task clustering for decomposing complex problems
into manageable task clusters.
"""
def __init__(self, config_path: str = "config/config.yaml"):
# Load configuration
with open(config_path, 'r') as f:
self.config = yaml.safe_load(f)['task_clustering']
self.max_cluster_size = self.config['max_cluster_size']
self.min_cluster_size = self.config['min_cluster_size']
self.similarity_threshold = self.config['similarity_threshold']
# Quantum backend setup
self.backend = AerSimulator()
self.shots = self.config['quantum_optimization']['shots']
# Initialize graph for symbolic relationships
self.task_graph = nx.Graph()
# Setup logging
logging.basicConfig(level=logging.INFO)
self.logger = logging.getLogger(__name__)
def _compute_task_embedding(self, task: Dict[str, Any]) -> np.ndarray:
"""
Compute the embedding for a task using foundation models.
Args:
task: Dictionary containing task description and metadata
Returns:
numpy.ndarray: Task embedding vector
"""
# TODO: Implement foundation model embedding
# This is a placeholder that returns random embeddings
return np.random.randn(768) # Using 768 as typical embedding dimension
def _quantum_similarity(self, vec1: np.ndarray, vec2: np.ndarray) -> float:
"""
Compute similarity between two vectors using quantum circuit.
Args:
vec1, vec2: Input vectors to compare
Returns:
float: Quantum-enhanced similarity score
"""
# Normalize vectors
vec1_norm = vec1 / np.linalg.norm(vec1)
vec2_norm = vec2 / np.linalg.norm(vec2)
# Create quantum circuit
qr = QuantumRegister(2, 'q')
cr = ClassicalRegister(1, 'c')
circuit = QuantumCircuit(qr, cr)
# Encode vectors into quantum states
# This is a simplified encoding - could be enhanced with more sophisticated methods
theta1 = np.arccos(vec1_norm[0])
theta2 = np.arccos(vec2_norm[0])
circuit.ry(2 * theta1, qr[0])
circuit.ry(2 * theta2, qr[1])
# Add interference
circuit.cx(qr[0], qr[1])
circuit.h(qr[0])
# Measure
circuit.measure(qr[0], cr[0])
# Execute circuit
job = self.backend.run(circuit, shots=self.shots)
result = job.result()
counts = result.get_counts(circuit)
# Compute similarity from measurement statistics
similarity = counts.get('0', 0) / self.shots
return similarity
def _build_symbolic_graph(self, tasks: List[Dict[str, Any]]) -> None:
"""
Build a graph representing symbolic relationships between tasks.
Args:
tasks: List of task dictionaries
"""
# Clear existing graph
self.task_graph.clear()
# Add nodes
for i, task in enumerate(tasks):
self.task_graph.add_node(i, task=task)
# Add edges based on symbolic relationships
for i in range(len(tasks)):
for j in range(i + 1, len(tasks)):
task1_embedding = self._compute_task_embedding(tasks[i])
task2_embedding = self._compute_task_embedding(tasks[j])
# Compute hybrid similarity
classical_sim = 1 - cosine(task1_embedding, task2_embedding)
quantum_sim = self._quantum_similarity(task1_embedding, task2_embedding)
# Combine classical and quantum similarities
hybrid_sim = 0.5 * (classical_sim + quantum_sim)
if hybrid_sim >= self.similarity_threshold:
self.task_graph.add_edge(i, j, weight=hybrid_sim)
def _optimize_clusters(self, clusters: List[TaskCluster]) -> List[TaskCluster]:
"""
Optimize cluster assignments using quantum optimization.
Args:
clusters: List of initial task clusters
Returns:
List[TaskCluster]: Optimized task clusters
"""
# TODO: Implement quantum optimization for cluster refinement
# This is a placeholder that returns the input clusters
return clusters
def decompose_problem(self, problem: Dict[str, Any]) -> List[TaskCluster]:
"""
Decompose a complex problem into quantum-symbolic task clusters.
Args:
problem: Dictionary containing problem description and requirements
Returns:
List[TaskCluster]: List of task clusters
"""
# Extract tasks from problem
tasks = self._extract_tasks(problem)
# Build symbolic relationship graph
self._build_symbolic_graph(tasks)
# Initial clustering using spectral clustering
n_clusters = max(
min(len(tasks) // self.min_cluster_size,
len(tasks) // self.max_cluster_size + 1),
1
)
# Use spectral clustering for initial grouping
clusters = []
if len(tasks) > 0:
# Convert graph to adjacency matrix
adj_matrix = nx.adjacency_matrix(self.task_graph).todense()
# Perform spectral clustering
from sklearn.cluster import SpectralClustering
clustering = SpectralClustering(
n_clusters=n_clusters,
affinity='precomputed',
random_state=42
).fit(adj_matrix)
# Create task clusters
for i in range(n_clusters):
cluster_tasks = [tasks[j] for j in range(len(tasks))
if clustering.labels_[j] == i]
if cluster_tasks:
# Compute cluster centroid
embeddings = [self._compute_task_embedding(task)
for task in cluster_tasks]
centroid = np.mean(embeddings, axis=0)
# Create cluster
cluster = TaskCluster(
id=f"cluster_{i}",
tasks=cluster_tasks,
centroid=centroid,
similarity_score=np.mean([
1 - cosine(emb, centroid) for emb in embeddings
])
)
clusters.append(cluster)
# Optimize clusters using quantum optimization
optimized_clusters = self._optimize_clusters(clusters)
return optimized_clusters
def _extract_tasks(self, problem: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
Extract individual tasks from a complex problem.
Args:
problem: Dictionary containing problem description and requirements
Returns:
List[Dict[str, Any]]: List of individual tasks
"""
# TODO: Implement task extraction logic
# This is a placeholder that assumes the problem contains a 'tasks' key
return problem.get('tasks', [])
def visualize_clusters(self, clusters: List[TaskCluster]) -> None:
"""
Visualize task clusters using networkx.
Args:
clusters: List of task clusters to visualize
"""
import matplotlib.pyplot as plt
plt.figure(figsize=(12, 8))
pos = nx.spring_layout(self.task_graph)
# Draw nodes
nx.draw_networkx_nodes(self.task_graph, pos)
# Draw edges with weights as colors
edges = self.task_graph.edges(data=True)
weights = [e[2]['weight'] for e in edges]
nx.draw_networkx_edges(self.task_graph, pos, edge_color=weights,
edge_cmap=plt.cm.viridis)
plt.title("Task Cluster Visualization")
plt.colorbar(plt.cm.ScalarMappable(cmap=plt.cm.viridis),
label="Similarity Score")
plt.show()
