"""
Neuro-Symbolic Autonomy Framework (NSAF) - Recursive Intent Projection
=====================================================================
Author: Bolorerdene Bundgaa
Contact: bolor@ariunbolor.org
Website: https://bolor.me
Recursive Intent Projection (RIP) implementation for continuous self-optimization,
intent-based learning, and multi-step projection planning.
"""
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.nn.functional as F
from torch.utils.data import Dataset, DataLoader
import networkx as nx
from datetime import datetime
import ray
@dataclass
class Intent:
"""Represents an agent's intent"""
id: str
description: str
goals: List[Dict[str, Any]]
constraints: List[Dict[str, Any]]
context: Dict[str, Any]
confidence: float
timestamp: float
parent_id: Optional[str] = None
@dataclass
class IntentProjection:
"""Represents a projected future intent state"""
intent: Intent
projected_state: Dict[str, Any]
success_probability: float
required_capabilities: List[str]
adaptation_steps: List[Dict[str, Any]]
class IntentGraph:
"""Graph structure for tracking intent evolution"""
def __init__(self):
self.graph = nx.DiGraph()
self.current_intent: Optional[Intent] = None
def add_intent(self, intent: Intent) -> None:
"""Add intent to graph"""
self.graph.add_node(intent.id,
intent=intent,
timestamp=intent.timestamp)
if intent.parent_id and intent.parent_id in self.graph:
self.graph.add_edge(intent.parent_id, intent.id)
self.current_intent = intent
def get_intent_history(self, steps: int = 5) -> List[Intent]:
"""Get recent intent history"""
if not self.current_intent:
return []
history = []
current = self.current_intent
while current and len(history) < steps:
history.append(current)
predecessors = list(self.graph.predecessors(current.id))
current = (self.graph.nodes[predecessors[0]]['intent']
if predecessors else None)
return history
class RecursiveIntentProjection:
"""
Implements Recursive Intent Projection (RIP) for continuous agent optimization
through intent-based learning and adaptation.
"""
def __init__(self, config_path: str = "config/config.yaml"):
# Load configuration
with open(config_path, 'r') as f:
self.config = yaml.safe_load(f)['recursive_intent']
# Initialize parameters
self.projection_depth = self.config['projection_depth']
self.confidence_threshold = self.config['confidence_threshold']
self.update_frequency = self.config['update_frequency']
self.max_iterations = self.config['max_iterations']
# Initialize components
self.intent_graph = IntentGraph()
self.intent_encoder = self._build_intent_encoder()
self.state_predictor = self._build_state_predictor()
self.adaptation_generator = self._build_adaptation_generator()
# Setup logging
logging.basicConfig(level=logging.INFO)
self.logger = logging.getLogger(__name__)
# Initialize Ray for distributed computing if not already initialized
if not ray.is_initialized():
ray.init()
def _build_intent_encoder(self) -> nn.Module:
"""Build neural encoder for intent representations"""
return nn.Sequential(
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, 128),
nn.ReLU(),
nn.Linear(128, 64)
)
def _build_state_predictor(self) -> nn.Module:
"""Build neural network for state prediction"""
return nn.Sequential(
nn.Linear(128, 256),
nn.ReLU(),
nn.Linear(256, 512),
nn.ReLU(),
nn.Linear(512, 256)
)
def _build_adaptation_generator(self) -> nn.Module:
"""Build neural network for generating adaptation steps"""
return nn.Sequential(
nn.Linear(320, 256), # 64 (intent) + 256 (state)
nn.ReLU(),
nn.Linear(256, 128),
nn.ReLU(),
nn.Linear(128, 64)
)
def _encode_intent(self, intent: Intent) -> torch.Tensor:
"""Encode intent into a vector representation"""
# Combine intent components into a feature vector
features = []
# Encode goals
goal_features = []
for goal in intent.goals:
# Convert goal dictionary to feature vector
# This is a simplified encoding - could be enhanced
goal_vec = torch.tensor([
float(goal.get('priority', 0.5)),
float(goal.get('complexity', 0.5)),
float(goal.get('progress', 0.0))
])
goal_features.append(goal_vec)
if goal_features:
goals_tensor = torch.stack(goal_features).mean(dim=0)
features.append(goals_tensor)
# Encode constraints
constraint_features = []
for constraint in intent.constraints:
# Convert constraint dictionary to feature vector
constraint_vec = torch.tensor([
float(constraint.get('importance', 0.5)),
float(constraint.get('strictness', 0.5))
])
constraint_features.append(constraint_vec)
if constraint_features:
constraints_tensor = torch.stack(constraint_features).mean(dim=0)
features.append(constraints_tensor)
# Add context features
context_vec = torch.tensor([
float(intent.context.get('complexity', 0.5)),
float(intent.context.get('urgency', 0.5)),
intent.confidence,
float(intent.timestamp) / 1e9 # Normalize timestamp
])
features.append(context_vec)
# Combine all features
combined_features = torch.cat(features)
# Pad or truncate to expected input dimension (512)
target_dim = 512
current_dim = combined_features.shape[0]
if current_dim < target_dim:
# Pad with zeros
padding = torch.zeros(target_dim - current_dim)
combined_features = torch.cat([combined_features, padding])
elif current_dim > target_dim:
# Truncate to target dimension
combined_features = combined_features[:target_dim]
# Ensure we have the correct shape and add batch dimension
combined_features = combined_features.unsqueeze(0) # Shape: [1, 512]
# Encode through neural network
return self.intent_encoder(combined_features.float())
def _predict_state(self, current_state: Dict[str, Any],
intent_encoding: torch.Tensor,
steps: int) -> List[Dict[str, Any]]:
"""Predict future states based on current state and intent"""
# Convert state values to floats, handling nested dicts
state_values = []
for v in current_state.values():
if isinstance(v, dict):
# For nested dicts, take a representative numeric value
if 'available' in v:
state_values.append(1.0 if v['available'] else 0.0)
else:
# Take the first numeric value or default to 0.5
numeric_val = next((val for val in v.values() if isinstance(val, (int, float))), 0.5)
state_values.append(float(numeric_val))
else:
state_values.append(float(v))
state_tensor = torch.tensor(state_values)
# Ensure intent_encoding is properly squeezed to 1D for concatenation
if intent_encoding.dim() > 1:
intent_encoding = intent_encoding.squeeze(0)
# Combine state and intent
combined = torch.cat([state_tensor, intent_encoding])
# Pad or truncate to expected dimension for state predictor (128)
target_dim = 128
current_dim = combined.shape[0]
if current_dim < target_dim:
# Pad with zeros
padding = torch.zeros(target_dim - current_dim)
combined = torch.cat([combined, padding])
elif current_dim > target_dim:
# Truncate to target dimension
combined = combined[:target_dim]
# Add batch dimension for neural network
combined = combined.unsqueeze(0) # Shape: [1, 128]
# Generate future states
future_states = []
current = combined
for _ in range(steps):
next_state = self.state_predictor(current)
future_states.append(next_state)
# Adjust dimensions for next iteration
if next_state.shape[1] != target_dim:
if next_state.shape[1] > target_dim:
current = next_state[:, :target_dim] # Truncate
else:
# Pad to target dimension
padding = torch.zeros(1, target_dim - next_state.shape[1])
current = torch.cat([next_state, padding], dim=1)
else:
current = next_state
# Convert tensors back to dictionaries
# Note: Since we may have padded/truncated, we'll create simplified states
return [
{
f"state_dim_{i}": state.squeeze(0)[i].item() if i < state.shape[1] else 0.0
for i in range(min(len(current_state), state.shape[1]))
}
for state in future_states
]
def _generate_adaptation_steps(self,
intent_encoding: torch.Tensor,
current_state: torch.Tensor,
target_state: torch.Tensor) -> List[Dict[str, Any]]:
"""Generate steps to adapt from current to target state"""
# Combine intent and state information
# Ensure all tensors are 1D for concatenation
if intent_encoding.dim() > 1:
intent_encoding = intent_encoding.squeeze(0)
if current_state.dim() > 1:
current_state = current_state.squeeze(0)
if target_state.dim() > 1:
target_state = target_state.squeeze(0)
combined = torch.cat([
intent_encoding,
current_state,
target_state
])
# Pad or truncate to expected dimension for adaptation generator (320)
target_dim = 320
current_dim = combined.shape[0]
if current_dim < target_dim:
# Pad with zeros
padding = torch.zeros(target_dim - current_dim)
combined = torch.cat([combined, padding])
elif current_dim > target_dim:
# Truncate to target dimension
combined = combined[:target_dim]
# Add batch dimension for neural network
combined = combined.unsqueeze(0) # Shape: [1, 320]
# Generate adaptation steps
adaptation_encoding = self.adaptation_generator(combined)
# Decode adaptation steps (simplified version)
# Remove batch dimension for processing
adaptation_encoding = adaptation_encoding.squeeze(0)
steps = []
step_size = 16 # Size of each adaptation step encoding
for i in range(0, len(adaptation_encoding), step_size):
step_encoding = adaptation_encoding[i:i+step_size]
# Convert encoding to adaptation step
step = {
'type': 'adjust_parameters', # Placeholder
'parameters': {
'learning_rate': float(step_encoding[0]),
'batch_size': int(step_encoding[1] * 128),
'optimization_weight': float(step_encoding[2])
},
'estimated_improvement': float(step_encoding[3])
}
steps.append(step)
return steps
def project_intent(self, current_intent: Intent,
current_state: Dict[str, Any]) -> List[IntentProjection]:
"""
Project future intent states and required adaptations.
Args:
current_intent: Current agent intent
current_state: Current agent state
Returns:
List[IntentProjection]: List of projected future states
"""
# Add current intent to graph
self.intent_graph.add_intent(current_intent)
# Encode current intent
intent_encoding = self._encode_intent(current_intent)
# Predict future states
future_states = self._predict_state(
current_state,
intent_encoding,
self.projection_depth
)
# Generate projections
projections = []
# Convert state values to floats, handling nested dicts
state_values = []
for v in current_state.values():
if isinstance(v, dict):
# For nested dicts, take a representative numeric value
if 'available' in v:
state_values.append(1.0 if v['available'] else 0.0)
else:
# Take the first numeric value or default to 0.5
numeric_val = next((val for val in v.values() if isinstance(val, (int, float))), 0.5)
state_values.append(float(numeric_val))
else:
state_values.append(float(v))
current_state_tensor = torch.tensor(state_values)
for i, future_state in enumerate(future_states):
# Create projected intent
projected_intent = Intent(
id=f"{current_intent.id}_proj_{i}",
description=f"Projected intent {i} steps from {current_intent.id}",
goals=current_intent.goals,
constraints=current_intent.constraints,
context=current_intent.context,
confidence=current_intent.confidence * (0.9 ** i), # Decay confidence
timestamp=datetime.now().timestamp(),
parent_id=current_intent.id
)
# Generate adaptation steps
# Convert future state values to floats, handling nested dicts
future_state_values = []
for v in future_state.values():
if isinstance(v, dict):
# For nested dicts, take a representative numeric value
if 'available' in v:
future_state_values.append(1.0 if v['available'] else 0.0)
else:
# Take the first numeric value or default to 0.5
numeric_val = next((val for val in v.values() if isinstance(val, (int, float))), 0.5)
future_state_values.append(float(numeric_val))
else:
future_state_values.append(float(v))
future_state_tensor = torch.tensor(future_state_values)
adaptation_steps = self._generate_adaptation_steps(
intent_encoding,
current_state_tensor,
future_state_tensor
)
# Create projection
projection = IntentProjection(
intent=projected_intent,
projected_state=future_state,
success_probability=max(0.0, 1.0 - (0.1 * i)), # Simple decay
required_capabilities=[
'learning',
'adaptation',
'self_optimization'
],
adaptation_steps=adaptation_steps
)
projections.append(projection)
return projections
def optimize_intent(self, current_intent: Intent,
projections: List[IntentProjection]) -> Intent:
"""
Optimize current intent based on projections.
Args:
current_intent: Current agent intent
projections: List of intent projections
Returns:
Intent: Optimized intent
"""
# Filter projections by confidence threshold
valid_projections = [
p for p in projections
if p.success_probability >= self.confidence_threshold
]
if not valid_projections:
return current_intent
# Select best projection
best_projection = max(valid_projections,
key=lambda p: p.success_probability)
# Create optimized intent
optimized_intent = Intent(
id=f"{current_intent.id}_opt_{int(datetime.now().timestamp())}",
description=f"Optimized intent from {current_intent.id}",
goals=current_intent.goals,
constraints=current_intent.constraints,
context={
**current_intent.context,
'optimization_steps': best_projection.adaptation_steps
},
confidence=best_projection.success_probability,
timestamp=datetime.now().timestamp(),
parent_id=current_intent.id
)
# Add to intent graph
self.intent_graph.add_intent(optimized_intent)
return optimized_intent
@ray.remote
def _parallel_project(self, intent: Intent,
state: Dict[str, Any]) -> List[IntentProjection]:
"""Parallel intent projection"""
return self.project_intent(intent, state)
def recursive_optimize(self, initial_intent: Intent,
initial_state: Dict[str, Any],
max_iterations: Optional[int] = None) -> Intent:
"""
Recursively optimize intent through multiple iterations.
Args:
initial_intent: Initial agent intent
initial_state: Initial agent state
max_iterations: Maximum number of optimization iterations
Returns:
Intent: Final optimized intent
"""
current_intent = initial_intent
iterations = 0
max_iter = max_iterations or self.max_iterations
while iterations < max_iter:
# Generate projections in parallel
future_refs = [
self._parallel_project.remote(current_intent, initial_state)
for _ in range(3) # Generate multiple projection sets
]
# Gather results
all_projections = []
for projections in ray.get(future_refs):
all_projections.extend(projections)
# Optimize intent
optimized_intent = self.optimize_intent(current_intent, all_projections)
# Check for convergence
if (optimized_intent.confidence >= self.confidence_threshold or
optimized_intent.confidence <= current_intent.confidence):
break
current_intent = optimized_intent
iterations += 1
return current_intent
def get_optimization_history(self) -> List[Dict[str, Any]]:
"""Get history of intent optimizations"""
history = []
for node_id in nx.topological_sort(self.intent_graph.graph):
node_data = self.intent_graph.graph.nodes[node_id]
intent = node_data['intent']
history.append({
'intent_id': intent.id,
'description': intent.description,
'confidence': intent.confidence,
'timestamp': intent.timestamp,
'parent_id': intent.parent_id
})
return history
