"""
Neuro-Symbolic Autonomy Framework (NSAF) - Human-AI Synergy
===========================================================
Author: Bolorerdene Bundgaa
Contact: bolor@ariunbolor.org
Website: https://bolor.me
Human-AI Synergy implementation for real-time cognitive synchronization,
adaptive state management, and seamless knowledge integration.
"""
from typing import List, Dict, Any, Optional, Tuple, Callable
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 datetime import datetime
import asyncio
import json
from queue import PriorityQueue
from threading import Lock
@dataclass
class CognitiveState:
"""Represents the cognitive state of an agent or human"""
id: str
attention: Dict[str, float] # Focus areas and their weights
context: Dict[str, Any] # Current context
goals: List[Dict[str, Any]] # Active goals
mental_model: Dict[str, Any] # Understanding of the situation
timestamp: float
confidence: float
@dataclass
class SyncEvent:
"""Represents a synchronization event between human and AI"""
id: str
human_state: CognitiveState
ai_state: CognitiveState
sync_type: str # 'knowledge', 'intent', 'action', etc.
timestamp: float
latency: float
success: bool
metrics: Dict[str, float]
class CognitiveBuffer:
"""Thread-safe buffer for cognitive states"""
def __init__(self, max_size: int = 1000):
self.max_size = max_size
self.buffer: PriorityQueue = PriorityQueue(maxsize=max_size)
self.lock = Lock()
def push(self, state: CognitiveState, priority: float) -> None:
"""Add state to buffer with priority"""
with self.lock:
if self.buffer.qsize() >= self.max_size:
self.buffer.get() # Remove lowest priority item
self.buffer.put((-priority, state)) # Negative for max-heap
def get_top_k(self, k: int) -> List[CognitiveState]:
"""Get top k states by priority"""
with self.lock:
states = []
temp_storage = []
# Get top k items
while len(states) < k and not self.buffer.empty():
priority, state = self.buffer.get()
states.append(state)
temp_storage.append((-priority, state))
# Restore items
for item in temp_storage:
self.buffer.put(item)
return states
class HumanAISynergy:
"""
Implements Human-AI Synergy for seamless cognitive synchronization
between AI agents and humans.
"""
def __init__(self, config_path: str = "config/config.yaml"):
# Load configuration
with open(config_path, 'r') as f:
self.config = yaml.safe_load(f)['human_ai_synergy']
# Initialize parameters
self.sync_interval = self.config['sync_interval']
self.max_latency = self.config['max_latency']
self.batch_size = self.config['batch_size']
self.priority_levels = self.config['priority_levels']
# Initialize components
self.cognitive_encoder = self._build_cognitive_encoder()
self.sync_predictor = self._build_sync_predictor()
self.state_buffer = CognitiveBuffer()
# Initialize metrics
self.metrics = {
'sync_success_rate': 0.0,
'avg_latency': 0.0,
'cognitive_alignment': 0.0
}
# Setup logging
logging.basicConfig(level=logging.INFO)
self.logger = logging.getLogger(__name__)
# Initialize async event loop
self.loop = asyncio.get_event_loop()
self.sync_task = None
def _build_cognitive_encoder(self) -> nn.Module:
"""Build neural encoder for cognitive states"""
return nn.Sequential(
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, 128),
nn.ReLU(),
nn.Linear(128, 64)
)
def _build_sync_predictor(self) -> nn.Module:
"""Build neural network for predicting sync success"""
return nn.Sequential(
nn.Linear(128, 64), # 64 (human) + 64 (ai)
nn.ReLU(),
nn.Linear(64, 32),
nn.ReLU(),
nn.Linear(32, 1),
nn.Sigmoid()
)
def _encode_cognitive_state(self, state: CognitiveState) -> torch.Tensor:
"""Encode cognitive state into vector representation"""
features = []
# Encode attention
attention_vec = torch.zeros(32) # Fixed size for attention
for i, (area, weight) in enumerate(state.attention.items()):
if i < 32:
attention_vec[i] = float(weight)
features.append(attention_vec)
# Encode context
context_vec = torch.tensor([
float(state.context.get('complexity', 0.5)),
float(state.context.get('urgency', 0.5)),
float(state.context.get('progress', 0.0))
])
features.append(context_vec)
# Encode goals
goal_vec = torch.zeros(32)
for i, goal in enumerate(state.goals):
if i < 32:
goal_vec[i] = float(goal.get('priority', 0.5))
features.append(goal_vec)
# Encode mental model
model_vec = torch.tensor([
float(state.mental_model.get('understanding', 0.5)),
float(state.mental_model.get('confidence', 0.5))
])
features.append(model_vec)
# Add metadata
meta_vec = torch.tensor([
state.confidence,
float(state.timestamp) / 1e9 # Normalize timestamp
])
features.append(meta_vec)
# Combine all features
combined = torch.cat(features)
# Encode through neural network
return self.cognitive_encoder(combined.float())
async def _sync_loop(self) -> None:
"""Continuous synchronization loop"""
while True:
try:
# Get recent states
human_states = self.state_buffer.get_top_k(self.batch_size)
if human_states:
# Process batch
for state in human_states:
await self._process_sync(state)
# Wait for next interval
await asyncio.sleep(self.sync_interval)
except Exception as e:
self.logger.error(f"Sync loop error: {e}")
await asyncio.sleep(1) # Back off on error
async def _process_sync(self, human_state: CognitiveState) -> None:
"""Process single synchronization event"""
start_time = datetime.now().timestamp()
try:
# Generate AI cognitive state
ai_state = await self._generate_ai_state(human_state)
# Predict sync success
human_encoding = self._encode_cognitive_state(human_state)
ai_encoding = self._encode_cognitive_state(ai_state)
combined = torch.cat([human_encoding, ai_encoding])
success_prob = self.sync_predictor(combined).item()
# Calculate latency
latency = datetime.now().timestamp() - start_time
# Create sync event
event = SyncEvent(
id=f"sync_{int(start_time * 1000)}",
human_state=human_state,
ai_state=ai_state,
sync_type='cognitive',
timestamp=start_time,
latency=latency,
success=success_prob > 0.5,
metrics={
'success_probability': success_prob,
'latency': latency,
'cognitive_alignment': self._calculate_alignment(
human_state, ai_state)
}
)
# Update metrics
self._update_metrics(event)
# Log event
self.logger.info(
f"Sync event {event.id}: success={event.success}, "
f"latency={event.latency:.3f}s"
)
except Exception as e:
self.logger.error(f"Sync processing error: {e}")
async def _generate_ai_state(self,
human_state: CognitiveState) -> CognitiveState:
"""Generate matching AI cognitive state"""
# This is a simplified version - could be enhanced with more sophisticated
# state generation based on the specific domain and requirements
return CognitiveState(
id=f"ai_{human_state.id}",
attention={k: v * 0.9 for k, v in human_state.attention.items()},
context=human_state.context.copy(),
goals=human_state.goals.copy(),
mental_model={
'understanding': human_state.mental_model.get('understanding', 0.5),
'confidence': human_state.mental_model.get('confidence', 0.5) * 0.9
},
timestamp=datetime.now().timestamp(),
confidence=human_state.confidence * 0.9
)
def _calculate_alignment(self, human_state: CognitiveState,
ai_state: CognitiveState) -> float:
"""Calculate cognitive alignment between human and AI states"""
# Attention alignment
attention_alignment = np.mean([
min(human_state.attention.get(k, 0), ai_state.attention.get(k, 0))
for k in set(human_state.attention) | set(ai_state.attention)
])
# Goal alignment
goal_alignment = len(set(str(g) for g in human_state.goals) &
set(str(g) for g in ai_state.goals)) / max(
len(human_state.goals), len(ai_state.goals), 1)
# Mental model alignment
model_alignment = np.mean([
min(human_state.mental_model.get(k, 0),
ai_state.mental_model.get(k, 0))
for k in set(human_state.mental_model) |
set(ai_state.mental_model)
])
# Combine alignments
return np.mean([
attention_alignment,
goal_alignment,
model_alignment
])
def _update_metrics(self, event: SyncEvent) -> None:
"""Update running metrics"""
alpha = 0.1 # Exponential moving average factor
self.metrics['sync_success_rate'] = (
(1 - alpha) * self.metrics['sync_success_rate'] +
alpha * float(event.success)
)
self.metrics['avg_latency'] = (
(1 - alpha) * self.metrics['avg_latency'] +
alpha * event.latency
)
self.metrics['cognitive_alignment'] = (
(1 - alpha) * self.metrics['cognitive_alignment'] +
alpha * event.metrics['cognitive_alignment']
)
def start_sync(self) -> None:
"""Start continuous synchronization"""
if self.sync_task is None:
self.sync_task = self.loop.create_task(self._sync_loop())
self.logger.info("Started cognitive synchronization")
def stop_sync(self) -> None:
"""Stop continuous synchronization"""
if self.sync_task is not None:
self.sync_task.cancel()
self.sync_task = None
self.logger.info("Stopped cognitive synchronization")
def update_human_state(self, state: CognitiveState) -> None:
"""Update human cognitive state"""
# Calculate priority based on state attributes
priority = (state.confidence * 0.4 +
float(state.context.get('urgency', 0.5)) * 0.3 +
max(g.get('priority', 0) for g in state.goals) * 0.3)
self.state_buffer.push(state, priority)
def get_metrics(self) -> Dict[str, float]:
"""Get current synchronization metrics"""
return self.metrics.copy()
def get_sync_status(self) -> Dict[str, Any]:
"""Get current synchronization status"""
return {
'active': self.sync_task is not None and not self.sync_task.done(),
'metrics': self.get_metrics(),
'buffer_size': self.state_buffer.buffer.qsize(),
'last_sync': datetime.now().timestamp()
}
