Terje
b028dc5311
- operator-brief.py: Decision surface with uncertainty thresholds - verification-queue.py: Evidence strength routing (was untracked) - mtp-development.md: MTP development tracking dossier Prepares for autonomous agent implementation per SOUL.md protocol
88 lines
2.7 KiB
Python
88 lines
2.7 KiB
Python
#!/usr/bin/env python3
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"""
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Operator Brief — Complex Decision Surfaces with Uncertainty Thresholds
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Handles multi-factor decisions with explicit uncertainty quantification.
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Delegates complex decisions when confidence < threshold.
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"""
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import json
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from datetime import datetime
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class OperatorBrief:
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def __init__(self, confidence_threshold=0.7):
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self.threshold = confidence_threshold
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self.decisions = []
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self.uncertainty_log = []
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def evaluate(self, decision, factors, uncertainty=0.0):
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"""
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Evaluate decision with explicit uncertainty.
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Args:
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decision: Decision string
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factors: Dict of contributing factors with weights
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uncertainty: Explicit uncertainty score (0.0-1.0)
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"""
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confidence = 1.0 - uncertainty
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if confidence >= self.threshold:
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# Direct decision
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result = {
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'decision': decision,
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'confidence': confidence,
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'factors': factors,
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'uncertainty': uncertainty,
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'timestamp': datetime.utcnow().isoformat(),
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'action': 'EXECUTE'
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}
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else:
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# Defer to higher-level analysis
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result = {
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'decision': decision,
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'confidence': confidence,
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'factors': factors,
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'uncertainty': uncertainty,
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'timestamp': datetime.utcnow().isoformat(),
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'action': 'DEFER',
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'reason': f'Confidence {confidence:.2f} < threshold {self.threshold:.2f}'
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}
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self.decisions.append(result)
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self.uncertainty_log.append(uncertainty)
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return result
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def aggregate_uncertainty(self):
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"""Return average uncertainty across all decisions."""
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if not self.uncertainty_log:
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return 0.0
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return sum(self.uncertainty_log) / len(self.uncertainty_log)
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def to_json(self):
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"""Export current state for logging."""
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return json.dumps({
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'decisions_count': len(self.decisions),
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'avg_uncertainty': self.aggregate_uncertainty(),
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'recent_decisions': self.decisions[-10:]
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}, indent=2)
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# Singleton instance
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operator_brief = OperatorBrief(confidence_threshold=0.7)
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if __name__ == "__main__":
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# Test usage
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result = operator_brief.evaluate(
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"Deploy MTP monitoring to production",
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{
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'monitoring_active': True,
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'data_collection': True,
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'resource_impact': 'moderate'
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},
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uncertainty=0.2
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)
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print(f"Decision: {result['action']}")
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print(f"Confidence: {result['confidence']:.2f}")
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