Model Adaptation · Software component
Rule Learner
Software componentModel AdaptationModelsVariation point (abstract)arc:RuleLearner
An abstract adaptation component that proposes new or refined if-then rules from labelled decision cases while preserving interpretable rule structure.
Responsibility. Generates candidate rule additions or refinements from experience.
Also known as: Rule learning component
Variants
| Variant | When to choose |
|---|---|
| Case-Based Rule Refiner | Choose when an expert-provided initial rule set exists and should be refined from accumulated cases where its decisions proved wrong. |
| Inductive Rule Learner | Choose when rules must be induced from historical labelled examples (e.g., past approved/denied applications) rather than from an existing expert rule set. |
Relationships
reads dependency
writes dependency
Design guidance
- MUST emit candidates in interpretable if-then form so experts can validate them.
- SHOULD balance generalisation against overfitting, checking candidates on held-out examples.
Classification
- Patterns
- Rule learningIncremental knowledge-base augmentation
- Quality attributes
- Flexibility (ISO/IEC 25010)Explainability (NIST AI RMF: explainable and interpretable)
- Risks mitigated
- Static rule base obsolescenceRepeated proposal of rejected rule patterns
Sources
- Ch5.11: T. Nguyen, "Rule-Based Decision Making Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.11. ISBN: 9798244538229.