Cognition · Software component
Discrepancy Significance Evaluator
Software componentCognitionCognition & Memoryarc:DiscrepancySignificanceEvaluator
A cognition component that statistically tests flagged discrepancies against sensor-noise and actuator-variability models, accumulating drift, and triggers replanning only when thresholds are exceeded.
Responsibility. Decides whether a detected discrepancy is noise or a problem warranting replanning.
Also known as: Replanning trigger, Noise-vs-problem classifier
Relationships
is configured by structural
writes dependency
receives data from dynamic
triggers dynamic
Design guidance
- MUST distinguish expected noise from significant discrepancies to prevent thrashing (replanning after every one or two actions).
- SHOULD track consecutive same-direction errors as systematic drift and escalate when accumulated error crosses thresholds.
Quantitative guidance
As stated by the sources; verify before use.
- Readings within 2 standard deviations (~95%) treated as noise; replanning triggered beyond ~3 standard deviations or 5 m absolute accumulated error (Ch5.6).
Classification
- Patterns
- Statistical significance testingAccumulated-drift detectionAdaptive thresholding
- Quality attributes
- Reliability (ISO/IEC 25010 | NIST AI RMF: valid and reliable)Performance efficiency (ISO/IEC 25010)
- Risks mitigated
- Replanning thrashing from noiseCompounding undetected drift
Sources
- Ch5.6: T. Nguyen, "A* Search and Replaning," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.6. ISBN: 9798244538229.