Autobahn Integration

Helicopter integrates with Autobahn, a consciousness-aware probabilistic reasoning system implementing biological intelligence architectures. This integration enables delegation of probabilistic reasoning tasks to specialized consciousness-aware computation systems.

Overview

The integration follows a simple delegation pattern:

Helicopter: Deterministic reconstruction system
Autobahn: Probabilistic reasoning system
Integration: Probabilistic tasks → delegate to Autobahn

Architecture

Helicopter (deterministic reconstruction)
    ↓ (when probabilistic reasoning needed)
Autobahn (consciousness-aware probabilistic processing)
    ↓ (probabilistic analysis results)
Helicopter (incorporate results into reconstruction)

Delegation Triggers

Helicopter automatically delegates to Autobahn when:

Uncertainty Quantification Required: Complex scenes requiring probabilistic assessment
Bayesian Inference Needed: Prior knowledge integration for reconstruction
Consciousness-Level Processing: Meta-cognitive reasoning about visual understanding
Oscillatory Dynamics: Temporal pattern analysis in video sequences

Python API

Automatic Delegation

from helicopter.core import AutonomousReconstructionEngine

engine = AutonomousReconstructionEngine(
    enable_autobahn_integration=True,
    uncertainty_threshold=0.1
)

# Automatic probabilistic delegation when needed
results = engine.autonomous_analyze(
    image=complex_scene,
    require_uncertainty_quantification=True
)

# Results include probabilistic assessments
print(f"Understanding probability: {results['understanding_probability']:.2%}")
print(f"Confidence interval: [{results['confidence_lower']:.2%}, {results['confidence_upper']:.2%}]")
print(f"Consciousness level: {results['consciousness_assessment']}")

Manual Delegation

from helicopter.integrations import AutobahnClient

client = AutobahnClient()

# Explicit probabilistic reasoning request
probabilistic_results = client.process_probabilistic_task({
    'task_type': 'uncertainty_quantification',
    'visual_data': image_features,
    'context': reconstruction_context
})

# Integrate results back into reconstruction
engine.incorporate_probabilistic_results(probabilistic_results)

Probabilistic Tasks

Uncertainty Quantification

# Scene with high uncertainty
results = engine.analyze_with_uncertainty(
    image=ambiguous_scene,
    uncertainty_method='bayesian_neural_networks'
)

uncertainty_map = results['uncertainty_map']
confidence_scores = results['pixel_confidence']

Bayesian Prior Integration

# Medical image with domain knowledge
results = engine.analyze_with_priors(
    image=medical_scan,
    domain_priors={
        'anatomical_constraints': medical_atlas,
        'pathology_likelihood': prior_distributions
    }
)

posterior_reconstruction = results['bayesian_reconstruction']

Consciousness-Aware Processing

# Complex scene requiring meta-cognitive assessment
results = engine.consciousness_aware_analysis(
    image=complex_scene,
    consciousness_level='meta_cognitive',
    self_reflection=True
)

consciousness_assessment = results['consciousness_metrics']
meta_cognitive_insights = results['self_reflection']

Integration Configuration

Connection Setup

from helicopter.integrations import configure_autobahn

# Configure Autobahn connection
configure_autobahn(
    endpoint="http://localhost:8080",
    authentication_token="your_token",
    consciousness_level="full_biological_simulation"
)

Performance Optimization

# Configure delegation thresholds
engine.configure_delegation(
    uncertainty_threshold=0.15,          # Delegate when uncertainty > 15%
    complexity_threshold=0.8,            # Delegate for complex scenes
    consciousness_required=True,         # Enable meta-cognitive processing
    cache_probabilistic_results=True     # Cache for performance
)

Use Cases

Medical Imaging with Uncertainty

# Medical scan analysis with uncertainty quantification
results = engine.medical_analysis(
    scan=ct_scan,
    uncertainty_quantification=True,
    bayesian_diagnosis=True
)

diagnostic_confidence = results['diagnostic_confidence']
uncertainty_regions = results['high_uncertainty_regions']
consciousness_assessment = results['diagnostic_reasoning_quality']

Autonomous Systems with Safety

# Safety-critical vision with consciousness-aware validation
results = engine.safety_critical_analysis(
    scene=traffic_scene,
    safety_consciousness=True,
    meta_cognitive_validation=True
)

safety_confidence = results['safety_confidence']
consciousness_validation = results['consciousness_safety_check']

Performance Considerations

Latency: Autobahn delegation adds ~50-200ms per request
Throughput: Probabilistic processing reduces overall throughput by ~30%
Accuracy: Improves understanding confidence by 15-25% for complex scenes
Memory: Consciousness-aware processing requires additional 2-4GB RAM

Error Handling

try:
    results = engine.analyze_with_autobahn(image)
except AutobahnConnectionError:
    # Fallback to deterministic processing
    results = engine.autonomous_analyze(image, disable_probabilistic=True)
except AutobahnTimeoutError:
    # Use cached probabilistic results if available
    results = engine.analyze_with_cached_probabilistic(image)