Functional domain: Adaptive Learning
Status: Stable — rule-based statistics and confidence updates; strategy preference on mission
abort/replan; no ML dependency.
Canonical architecture: cognitive-resilience-architecture.md Recovery learning detail: adaptive-recovery.md
Improve operational decisions using historical outcomes. Adaptive operations cover recovery strategy selection, confidence updates, and historical recommendations — implemented as transparent rules and statistics, not opaque models.
| Capability | Implementation |
|---|---|
| Rule-based adaptation | AdaptiveRecoveryPolicy success-rate thresholds |
| Statistics | StrategySuccessRate, rolling attempt counts |
| Confidence updates | RecoveryConfidence.score from history |
| Historical recommendations | POST /v1/recovery/recommend |
| Field | Description |
|---|---|
Entity.recovery_confidence |
EntityRecoveryConfidence — score, preferred_strategy, attempts |
RecoveryConfidence, RecoveryHistory, StrategySuccessRate, StrategyPreference,
AdaptiveRecoveryPolicy
- Camera reconnect succeeds 3/3 times → prefer reconnect strategy
- Provider restart fails repeatedly → escalate sooner in recommend
- Robot replacement faster than retry → prefer takeover in continuity
- Fusion confidence low after recovery → lower recovery confidence score
spanda recovery confidence
spanda recovery learning-report| Surface | Endpoint |
|---|---|
| Recovery metrics | GET /v1/recovery/metrics → recovery_confidence |
| Recovery recommend | POST /v1/recovery/recommend |
| Entity autonomy | GET /v1/entities/{id}/autonomy → recovery_confidence |
Recovery Confidence metric in the Cognitive & Resilience tab.
- Recovery Orchestrator: history feeds
compute_recovery_confidence() - Attention Engine: failed recovery patterns may escalate attention
- Operational Memory: episodic recovery history in playbooks/traces
ML-backed strategy selection may arrive via official packages — the functional domain boundary remains the same; only the recommendation backend would change.