BEHAVIOR-001: learn and predict actuator actions

app/actuators/models.py

@@ -25,6 +25,18 @@ class LifecycleStatus(StrEnum):
     ARCHIVED = "archived"
 
 
+class BehaviorMode(StrEnum):
+    SHADOW = "shadow"
+    ACTIVE = "active"
+    PAUSED = "paused"
+
+
+class BehaviorStatus(StrEnum):
+    COLLECTING = "collecting"
+    TRAINED = "trained"
+    BLOCKED = "blocked"
+
+
 class AssignmentCandidate(BaseModel):
     entity_id: str
     domain: str
@@ -71,10 +83,49 @@ class ModelLifecycleState(BaseModel):
     last_history_signature: str | None = None
     last_history_point_count: int = Field(default=0, ge=0)
     reason: str = "Noch keine Trainingsdaten ausgewertet."
-    next_action: str = "Aktuator auswählen und Zuordnung prüfen."
+    next_action: str = "Aktor auswählen; Kontext und Historie werden automatisch geprüft."
     audit: list[LifecycleAuditEntry] = Field(default_factory=list)
 
 
+class BehaviorPattern(BaseModel):
+    target_state: str = Field(min_length=1, max_length=100)
+    minute_of_day: int = Field(ge=0, le=1439)
+    weekday: int = Field(ge=0, le=6)
+    context_states: dict[str, str] = Field(default_factory=dict)
+    source: str = Field(default="observed", max_length=40)
+    weight: float = Field(default=1.0, ge=0.1, le=1.0)
+    observed_at: datetime
+
+
+class BehaviorPrediction(BaseModel):
+    target_state: str
+    confidence: float = Field(ge=0.0, le=1.0)
+    generated_at: datetime
+    reason: str
+    matching_patterns: int = Field(default=0, ge=0)
+    executed: bool = False
+
+
+class ExecutionEvent(BaseModel):
+    target_state: str
+    executed_at: datetime
+
+
+class BehaviorState(BaseModel):
+    mode: BehaviorMode = BehaviorMode.SHADOW
+    status: BehaviorStatus = BehaviorStatus.COLLECTING
+    approved_at: datetime | None = None
+    sample_count: int = Field(default=0, ge=0)
+    high_confidence_sample_count: int = Field(default=0, ge=0)
+    patterns: list[BehaviorPattern] = Field(default_factory=list)
+    prediction: BehaviorPrediction | None = None
+    last_trained_at: datetime | None = None
+    last_evaluated_at: datetime | None = None
+    last_executed_at: datetime | None = None
+    execution_events: list[ExecutionEvent] = Field(default_factory=list)
+    reason: str = "Historische Aktorhandlungen werden analysiert."
+
+
 class ActuatorRecord(BaseModel):
     actuator_entity_id: str = Field(pattern=r"^[a-z0-9_]+\.[a-z0-9_]+$")
     enabled: bool = True
@@ -85,6 +136,7 @@ class ActuatorRecord(BaseModel):
     numeric_candidates: list[AssignmentCandidate] = Field(default_factory=list)
     context_candidates: list[AssignmentCandidate] = Field(default_factory=list)
     lifecycle: ModelLifecycleState
+    behavior: BehaviorState = Field(default_factory=BehaviorState)
 
 
 class ReconciliationState(BaseModel):