Merge pull request 'v0.6.0: kausales Shadow-Lernen aus Sensorwechseln' (#37) from feature/causal-shadow-v0.6.0 into main

Merge pull request v0.5.0 behavior learning and safe activation (#32)

CHANGELOG.md

@@ -1,5 +1,42 @@
 # Changelog
 
+## 0.6.0 - 2026-06-14
+- Kausales Shadow-Lernen erkennt frische Kontextwechsel unmittelbar vor einer
+  Aktorhandlung, etwa `Tür geschlossen → offen` vor `Licht aus → an`
+- Historische Home-Assistant-Automationen dürfen Vorhersagen begründen, zählen
+  aber weiterhin niemals als eindeutige Benutzerhandlung oder Ausführungsfreigabe
+- Aktuelle `last_changed`-Zeitpunkte verhindern Vorhersagen aus längst
+  unveränderten Sensorzuständen
+- Oberfläche trennt gelernte Benutzerhandlungen und erkannte HA-Automationen
+
+## 0.5.4 - 2026-06-14
+- Tür-, Bewegungs- und andere belastbare Kontextsensoren werden auch ohne
+  numerischen Sensor als vollständige automatische Kontextzuordnung angezeigt
+- Status und Zuordnungssicherheit bilden das aktive Verhaltenslernen ab statt
+  eines optionalen numerischen Modells
+- Ausführungsfreigabe erscheint erst, wenn genügend eindeutig manuelle
+  Bedienungen vorliegen; bis dahin nennt die Oberfläche die noch fehlende Anzahl
+
+## 0.5.3 - 2026-06-14
+- Verhindert fachlich falsche Sensorzuordnungen nur aufgrund generischer Namen wie
+  `Licht` oder `Lichtschalter`
+- Übernimmt numerische Sensoren nur noch bei einem belastbaren absoluten Score und
+  einer eindeutigen Abgrenzung zum zweitbesten Kandidaten
+- Begrenzt Zusatzkontext auf relevante Sensoren und bevorzugt bei Lichtaktoren
+  echte Beleuchtungsstärke gegenüber fremden Leistungs- oder Energiezählern
+
+## 0.5.2 - 2026-06-14
+- Add-on-Build invalidiert den Docker-Cache bei jeder Versionsänderung, damit
+  Versionsmetadaten und tatsächlich ausgelieferter Anwendungscode übereinstimmen
+- Korrigierte Ingress-Oberfläche aus 0.5.1 dadurch erstmals zuverlässig ausgeliefert
+
+## 0.5.1 - 2026-06-14
+- Technische Modell-, Intervall- und Sicherheitsparameter aus der normalen
+  Home-Assistant-Add-on-Konfiguration entfernt; sichere Standardwerte bleiben aktiv
+- Ingress um einen klaren Ablauf mit Aktorauswahl, Beobachtungsphase und späterer
+  Ausführungsfreigabe ergänzt
+- Bedienelemente und Diagnosen in verständlicher Alltagssprache erklärt
+
 ## 0.5.0 - 2026-06-14
 - Ingress auf reine Aktorauswahl, automatischen Lernstatus und Vorhersagen reduziert
 - Automatische Kontextzuordnung ohne Sensor-Overrides oder Review-Blockade

addon/Dockerfile

@@ -4,13 +4,17 @@ ENV PYTHONDONTWRITEBYTECODE=1 \
     PYTHONUNBUFFERED=1 \
     PIP_NO_CACHE_DIR=1
 
+# The add-on version changes for every release. Copying its config before the
+# clone makes Docker invalidate the application layer instead of reusing old code.
+COPY config.yaml /tmp/addon-config.yaml
+
 RUN apt-get update \
     && apt-get install -y --no-install-recommends git \
     && git clone --depth 1 --branch main \
        http://192.168.6.31:3000/pino/sillyhome-next.git /app \
     && python -m pip install --upgrade pip \
     && python -m pip install /app \
-    && rm -rf /var/lib/apt/lists/* /app/.git
+    && rm -rf /var/lib/apt/lists/* /app/.git /tmp/addon-config.yaml
 
 COPY run.sh /run.sh
 RUN chmod 0755 /run.sh

addon/config.yaml

@@ -1,5 +1,5 @@
 name: SillyHome Next
-version: "0.5.0"
+version: "0.6.0"
 slug: sillyhome_next
 description: Lernt automatisch aus deinem Verhalten und steuert freigegebene Aktoren
 url: http://192.168.6.31:3000/pino/sillyhome-next
@@ -16,28 +16,6 @@ panel_admin: true
 homeassistant_api: true
 hassio_api: false
 auth_api: false
-options:
-  history_days: 14
-  min_training_points: 24
-  retrain_stale_hours: 24
-  reconcile_interval_seconds: 900
-  min_behavior_actions: 3
-  prediction_confidence: 0.82
-  prediction_window_minutes: 30
-  prediction_interval_seconds: 60
-  execution_cooldown_seconds: 900
-  timezone: Europe/Berlin
-schema:
-  history_days: "int(1,31)"
-  min_training_points: "int(2,10000)"
-  retrain_stale_hours: "int(1,720)"
-  reconcile_interval_seconds: "int(60,86400)"
-  min_behavior_actions: "int(2,100)"
-  prediction_confidence: "float(0.5,0.99)"
-  prediction_window_minutes: "int(5,120)"
-  prediction_interval_seconds: "int(30,3600)"
-  execution_cooldown_seconds: "int(60,86400)"
-  timezone: "str"
 map:
   - type: addon_config
     read_only: false

app/actuators/lifecycle.py

@@ -45,6 +45,8 @@ _STOPWORDS = frozenset(
         "humidity",
         "illuminance",
         "light",
+        "licht",
+        "lichtschalter",
         "power",
         "sensor",
         "state",
@@ -54,8 +56,10 @@ _STOPWORDS = frozenset(
     }
 )
 _NUMERIC_AUTO_ACCEPT_SCORE = 0.82
+_NUMERIC_AUTO_ACCEPT_MIN_SCORE = 0.5
 _NUMERIC_MIN_MARGIN = 0.18
 _CONTEXT_AUTO_ACCEPT_SCORE = 0.78
+_CONTEXT_AUTO_ACCEPT_MIN_SCORE = 0.3
 _MAX_CONTEXT_SELECTIONS = 5
 _AUDIT_LIMIT = 20
 
@@ -231,12 +235,29 @@ class ActuatorReconciliationService:
         numeric_candidates: list[AssignmentCandidate],
         context_candidates: list[AssignmentCandidate],
     ) -> AssignmentSelection:
-        top_numeric = numeric_candidates[0] if numeric_candidates else None
+        top_numeric = next(
-        top_contexts = [
+            (candidate for candidate in numeric_candidates if candidate.auto_accepted),
-            candidate.entity_id
+            None,
+        )
+        accepted_contexts = [
+            candidate
             for candidate in context_candidates
+            if candidate.auto_accepted
         ][: _MAX_CONTEXT_SELECTIONS]
+        top_contexts = [candidate.entity_id for candidate in accepted_contexts]
         if top_numeric is None:
+            if accepted_contexts:
+                return AssignmentSelection(
+                    selected_numeric_entity_id=None,
+                    selected_context_entity_ids=top_contexts,
+                    source=AssignmentSource.AUTOMATIC,
+                    confidence=max(candidate.confidence for candidate in accepted_contexts),
+                    review_required=False,
+                    reason=(
+                        "Passender Schaltkontext automatisch erkannt. Für diese "
+                        "Verhaltensvorhersage ist kein numerischer Sensor erforderlich."
+                    ),
+                )
             return AssignmentSelection(
                 selected_numeric_entity_id=None,
                 selected_context_entity_ids=top_contexts,
@@ -433,8 +454,15 @@ class ActuatorReconciliationService:
             confidence = candidate.score / highest if highest else 0.0
             margin = candidate.score - second_score if index == 0 else 0.0
             auto_score = _CONTEXT_AUTO_ACCEPT_SCORE if context else _NUMERIC_AUTO_ACCEPT_SCORE
-            auto_accepted = confidence >= auto_score and (
+            minimum_score = (
-                context or margin >= _NUMERIC_MIN_MARGIN
+                _CONTEXT_AUTO_ACCEPT_MIN_SCORE
+                if context
+                else _NUMERIC_AUTO_ACCEPT_MIN_SCORE
+            )
+            auto_accepted = (
+                candidate.score >= minimum_score
+                and confidence >= auto_score
+                and (context or margin >= _NUMERIC_MIN_MARGIN)
             )
             sorted_candidates[index] = candidate.model_copy(
                 update={
@@ -510,6 +538,9 @@ def _score_candidate(
     if entity.device_class in preferred_device_classes:
         score += 0.2
         evidence.append(f"Passende device_class: {entity.device_class}")
+    if not context and actuator.domain == "light" and entity.device_class == "illuminance":
+        score += 0.2
+        evidence.append("Beleuchtungsstärke wird für Lichtaktoren bevorzugt.")
     if not context and entity.unit_of_measurement is not None:
         score += 0.05
         evidence.append(f"Numerische Einheit vorhanden: {entity.unit_of_measurement}")

app/actuators/models.py

@@ -92,6 +92,9 @@ class BehaviorPattern(BaseModel):
     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)
+    trigger_entity_id: str | None = None
+    trigger_from_state: str | None = None
+    trigger_to_state: str | None = None
     source: str = Field(default="observed", max_length=40)
     weight: float = Field(default=1.0, ge=0.1, le=1.0)
     observed_at: datetime

app/behavior/engine.py

@@ -22,6 +22,7 @@ from app.ha.reader import HaReader
 _MAX_PATTERNS = 500
 _MAX_EXECUTION_EVENTS = 100
 _ACTION_LOGBOOK_TOLERANCE = timedelta(seconds=10)
+_CONTEXT_TRIGGER_TOLERANCE = timedelta(seconds=3)
 _OWN_ACTION_TOLERANCE = timedelta(seconds=20)
 _SAFE_ACTIVE_DOMAINS = frozenset({"cover", "fan", "humidifier", "light", "switch"})
 _AUTOMATION_CONTEXT_DOMAINS = frozenset({"automation", "script"})
@@ -198,12 +199,18 @@ class BehaviorEngine:
             )
             if entity_id and entity_id in entities and entities[entity_id].state is not None
         }
+        current_context_changed_at = {
+            entity_id: entities[entity_id].last_changed
+            for entity_id in current_context
+        }
         prediction = predict_behavior(
             record.behavior.patterns,
             current_context=current_context,
+            current_context_changed_at=current_context_changed_at,
             now=now,
             min_support=self._settings.min_behavior_actions,
             window_minutes=self._settings.prediction_window_minutes,
+            causal_window_seconds=self._settings.prediction_interval_seconds * 2,
             timezone_name=self._settings.timezone,
         )
         behavior = record.behavior.model_copy(
@@ -326,7 +333,12 @@ class BehaviorEngine:
             if _matches_own_execution(point, own_executions):
                 continue
             source, weight = _action_source(point, logbook)
-            if source == "automation":
+            trigger = _recent_context_transition(
+                context_history,
+                context_ids,
+                point.timestamp,
+            )
+            if source == "automation" and trigger is None:
                 continue
             contexts = {
                 entity_id: state
@@ -340,6 +352,9 @@ class BehaviorEngine:
                     minute_of_day=local.hour * 60 + local.minute,
                     weekday=local.weekday(),
                     context_states=contexts,
+                    trigger_entity_id=trigger[0] if trigger else None,
+                    trigger_from_state=trigger[1] if trigger else None,
+                    trigger_to_state=trigger[2] if trigger else None,
                     source=source,
                     weight=weight,
                     observed_at=point.timestamp,
@@ -373,14 +388,52 @@ def predict_behavior(
     now: datetime,
     min_support: int,
     window_minutes: int,
+    current_context_changed_at: dict[str, datetime | None] | None = None,
+    causal_window_seconds: int = 120,
     timezone_name: str = "Europe/Berlin",
 ) -> BehaviorPrediction | None:
     if not patterns:
         return None
     local = now.astimezone(ZoneInfo(timezone_name))
     minute_of_day = local.hour * 60 + local.minute
+    changed_at = current_context_changed_at or {}
     by_state: dict[str, list[float]] = {}
+    causal_support_by_state: dict[str, int] = {}
     for pattern in patterns:
+        if pattern.trigger_entity_id and pattern.trigger_to_state:
+            trigger_changed_at = changed_at.get(pattern.trigger_entity_id)
+            trigger_age = (
+                (now - trigger_changed_at).total_seconds()
+                if trigger_changed_at is not None
+                else None
+            )
+            if not (
+                current_context.get(pattern.trigger_entity_id)
+                == pattern.trigger_to_state
+                and trigger_age is not None
+                and 0 <= trigger_age <= causal_window_seconds
+            ):
+                continue
+            comparable = [
+                (entity_id, expected)
+                for entity_id, expected in pattern.context_states.items()
+                if entity_id in current_context
+            ]
+            context_score = (
+                sum(
+                    current_context[entity_id] == expected
+                    for entity_id, expected in comparable
+                )
+                / len(comparable)
+                if comparable
+                else 0.5
+            )
+            score = pattern.weight * (0.85 + 0.15 * context_score)
+            by_state.setdefault(pattern.target_state, []).append(score)
+            causal_support_by_state[pattern.target_state] = (
+                causal_support_by_state.get(pattern.target_state, 0) + 1
+            )
+            continue
         distance = _circular_minute_distance(minute_of_day, pattern.minute_of_day)
         if distance > window_minutes:
             continue
@@ -414,6 +467,7 @@ def predict_behavior(
         key=lambda item: (sum(item[1]), len(item[1]), item[0]),
     )
     support = len(scores)
+    causal_support = causal_support_by_state.get(target_state, 0)
     confidence = min(1.0, (sum(scores) / support) * min(1.0, support / min_support))
     if confidence <= 0:
         return None
@@ -423,7 +477,12 @@ def predict_behavior(
         generated_at=now,
         matching_patterns=support,
         reason=(
-            f"{support} ähnliche Handlungsmuster passen zu Zeit und aktuellem Kontext."
+            (
+                f"{causal_support} historische Handlungen folgten demselben "
+                "frischen Sensorwechsel."
+            )
+            if causal_support
+            else f"{support} ähnliche Handlungsmuster passen zu Zeit und aktuellem Kontext."
         ),
     )
 
@@ -476,6 +535,32 @@ def _matches_own_execution(
     )
 
 
+def _recent_context_transition(
+    history: dict[str, StateHistorySeries],
+    context_ids: list[str],
+    timestamp: datetime,
+) -> tuple[str, str, str] | None:
+    nearest: tuple[timedelta, str, str, str] | None = None
+    for entity_id in context_ids:
+        series = history.get(entity_id)
+        if series is None:
+            continue
+        previous_state: str | None = None
+        for point in series.points:
+            if point.timestamp > timestamp:
+                break
+            if previous_state is not None and point.state != previous_state:
+                age = timestamp - point.timestamp
+                if age <= _CONTEXT_TRIGGER_TOLERANCE and (
+                    nearest is None or age < nearest[0]
+                ):
+                    nearest = (age, entity_id, previous_state, point.state)
+            previous_state = point.state
+    if nearest is None:
+        return None
+    return nearest[1], nearest[2], nearest[3]
+
+
 def _circular_minute_distance(left: int, right: int) -> int:
     direct = abs(left - right)
     return min(direct, 1440 - direct)

app/ha/models.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+from datetime import datetime
+
 from pydantic import BaseModel
 
 
@@ -15,6 +17,7 @@ class HaEntitySummary(BaseModel):
     entity_id: str
     domain: str
     state: str | None = None
+    last_changed: datetime | None = None
     state_class: str | None = None
     device_class: str | None = None
     unit_of_measurement: str | None = None

app/ha/reader.py

@@ -53,6 +53,7 @@ class HaReader:
                     entity_id=entity_id,
                     domain=domain,
                     state=_optional_str(item.get("state")),
+                    last_changed=_optional_datetime(item.get("last_changed")),
                     state_class=_optional_str(attributes.get("state_class")),
                     device_class=_optional_str(attributes.get("device_class")),
                     unit_of_measurement=_optional_str(attributes.get("unit_of_measurement")),
@@ -116,3 +117,13 @@ def _optional_str(value: object) -> str | None:
     if value is None or value == "":
         return None
     return str(value)
+
+
+def _optional_datetime(value: object) -> datetime | None:
+    if not isinstance(value, str) or not value:
+        return None
+    try:
+        parsed = datetime.fromisoformat(value.replace("Z", "+00:00"))
+    except ValueError:
+        return None
+    return parsed if parsed.tzinfo is not None else None

app/main.py

@@ -77,7 +77,7 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
 app = FastAPI(
     title="SillyHome Next API",
     description="Lokales Smart-Home-Intelligenzsystem für Home Assistant.",
-    version="0.5.0",
+    version="0.5.2",
     lifespan=lifespan,
 )
 app.state.settings = load_settings()

app/static/index.html

@@ -13,6 +13,10 @@
     main { display: grid; grid-template-columns: repeat(auto-fit,minmax(320px,1fr)); gap: 14px; padding: 14px; }
     section { background: #18212b; border: 1px solid #2d3a47; border-radius: 12px; padding: 16px; }
     .wide { grid-column: 1 / -1; }
+    .steps { display:grid; grid-template-columns:repeat(auto-fit,minmax(220px,1fr)); gap:12px; }
+    .step { background:#111a23; border:1px solid #31404d; border-radius:10px; padding:14px; }
+    .step-number { display:inline-grid; place-items:center; width:28px; height:28px; border-radius:50%; background:#23715b; font-weight:700; margin-bottom:8px; }
+    .step p { margin:5px 0; }
     .ok { color: #66dfa9; }
     .warn { color: #f3c969; }
     .bad { color: #ff8f8f; }
@@ -34,34 +38,61 @@
 <body>
 <header>
   <h1>SillyHome Next</h1>
-  <p>Du wählst nur die Aktoren. SillyHome findet Kontext, lernt Gewohnheiten und trifft Vorhersagen im Shadow-Modus.</p>
+  <p>Hier wählst du nur Geräte aus, deren Bedienung SillyHome lernen soll. Sensoren, Zusammenhänge und Modelle werden automatisch verwaltet.</p>
-  <p class="notice">Geschaltet wird erst nach deiner ausdrücklichen Freigabe pro Aktor.</p>
+  <p class="notice">Sicherer Start: Zuerst wird nur beobachtet und vorhergesagt. Ohne deine spätere Freigabe wird nichts geschaltet.</p>
 </header>
 <main>
+  <section class="wide">
+    <h2>So gehst du vor</h2>
+    <div class="steps">
+      <div class="step">
+        <span class="step-number">1</span>
+        <h3>Aktor auswählen</h3>
+        <p><strong>Wo?</strong> Unten im Feld „Gerät auswählen“.</p>
+        <p><strong>Was passiert?</strong> SillyHome ordnet Raum, Sensoren, Zustände und vorhandene Historie automatisch zu.</p>
+      </div>
+      <div class="step">
+        <span class="step-number">2</span>
+        <h3>Wie gewohnt bedienen</h3>
+        <p><strong>Wo?</strong> Weiterhin in Home Assistant, an Schaltern oder über deine bisherigen Bedienwege.</p>
+        <p><strong>Was passiert?</strong> SillyHome lernt deine Handlungen und zeigt Vorhersagen an, schaltet aber noch nicht selbst.</p>
+      </div>
+      <div class="step">
+        <span class="step-number">3</span>
+        <h3>Später freigeben</h3>
+        <p><strong>Wo?</strong> In den Details des ausgewählten Geräts, sobald genug Verhalten gelernt wurde.</p>
+        <p><strong>Was passiert?</strong> Erst dann darf SillyHome passende Vorhersagen automatisch ausführen. Die Freigabe kann jederzeit gestoppt werden.</p>
+      </div>
+    </div>
+  </section>
+
   <section>
     <h2>Systemstatus</h2>
+    <p class="muted">Zeigt, ob Verbindung, Lernsystem und automatische Prüfungen funktionieren. Hier musst du normalerweise nichts einstellen.</p>
     <div id="status">Prüfung läuft ...</div>
     <div class="chips" id="status-chips"></div>
     <button class="secondary" onclick="loadOverview()">Status aktualisieren</button>
   </section>
 
   <section>
-    <h2>Aktor freigeben</h2>
+    <h2>1. Gerät zum Lernen auswählen</h2>
-    <p class="muted">Nach der Auswahl analysiert SillyHome automatisch passende Sensoren, Zustände und Historie.</p>
+    <p class="muted">Wähle eine Lampe, einen Rollladen oder einen anderen unterstützten Aktor. Du wählst keine Sensoren und erstellst keine Regeln.</p>
-    <label for="actuator-select">Home-Assistant-Aktor</label>
+    <label for="actuator-select">Gerät aus Home Assistant</label>
     <select id="actuator-select"></select>
-    <button onclick="configureActuator()">Auswählen und Lernen starten</button>
+    <button onclick="configureActuator()">Gerät hinzufügen und Beobachtung starten</button>
     <p id="actuator-config-result" class="muted">Noch kein Aktor ausgewählt.</p>
   </section>
 
   <section class="wide">
-    <h2>Ausgewählte Aktoren</h2>
+    <h2>2. Beobachtete Geräte</h2>
+    <p class="muted">Öffne „Details“, um Lernfortschritt, aktuelle Vorhersage und den automatisch gefundenen Kontext zu sehen.</p>
     <div id="configured-actuators">Noch nicht geladen.</div>
   </section>
 
   <section class="wide">
-    <h2>Automatisch erkannter Lernkontext</h2>
+    <h2>3. Lernfortschritt und Freigabe</h2>
-    <div id="actuator-detail" class="muted">Wähle einen Aktor aus der Liste.</div>
+    <p class="muted">Die Freigabe erscheint erst, wenn genug eindeutig zugeordnete Handlungen gelernt wurden. Vorher bleibt das Gerät sicher im Beobachtungsmodus.</p>
+    <div id="actuator-detail" class="muted">Öffne bei einem beobachteten Gerät die Details.</div>
   </section>
 </main>
 <script>
@@ -81,6 +112,7 @@ async function api(path, options = {}) {
 }
 
 function lifecycleLabel(record) {
+  if (record.behavior.status === "trained") return "Kontext erkannt";
   const labels = {
     trained: "lernt",
     pending_history: "sammelt Historie",
@@ -93,6 +125,7 @@ function lifecycleLabel(record) {
 }
 
 function statusClass(record) {
+  if (record.behavior.status === "trained") return "ok";
   if (record.lifecycle.status === "trained") return "ok";
   if (["pending_history", "pending_assignment", "archived"].includes(record.lifecycle.status)) return "warn";
   return "bad";
@@ -120,7 +153,7 @@ async function loadOverview() {
       `<span class="chip">API: ${escapeHtml(health.status)}</span>`,
       `<span class="chip">Lernsystem: ${escapeHtml(ml.status)}</span>`,
       `<span class="chip">Aktoren: ${actuators.length}</span>`,
-      `<span class="chip">Aktive Modelle: ${reconciliation.trained_models}</span>`,
+      `<span class="chip">Lernbereite Geräte: ${reconciliation.trained_models}</span>`,
     ].join("");
   } catch (error) {
     status.innerHTML = `<p class="bad">${escapeHtml(error.message)}</p>`;
@@ -171,7 +204,7 @@ async function loadConfiguredActuators() {
     const rows = await api("v1/actuators");
     box.innerHTML = rows.length ? `
       <table>
-        <tr><th>Aktor</th><th>Verhaltensmodell</th><th>Handlungen</th><th>Vorhersage</th><th></th></tr>
+        <tr><th>Gerät</th><th>Lernstatus</th><th>Gelernte Handlungen</th><th>Letzte Vorhersage</th><th>Aktionen</th></tr>
         ${rows.map(record => `
           <tr>
             <td>${escapeHtml(record.actuator_entity_id)}</td>
@@ -206,36 +239,48 @@ async function showActuator(actuatorId) {
       .map(candidate => `<li><strong>${escapeHtml(candidate.friendly_name || candidate.entity_id)}</strong>: ${candidate.evidence.map(escapeHtml).join(", ") || "statistisch relevanter Kandidat"}</li>`)
       .join("");
     const prediction = record.behavior.prediction;
+    const requiredUserActions = 3;
+    const learnedAutomationActions = record.behavior.patterns.filter(
+      pattern => pattern.source === "automation",
+    ).length;
+    const missingUserActions = Math.max(
+      0,
+      requiredUserActions - record.behavior.high_confidence_sample_count,
+    );
     const activationButton = record.behavior.mode === "active"
       ? `<button class="danger" onclick="setActivation('${escapeHtml(record.actuator_entity_id)}', false)">Autonomes Schalten stoppen</button>`
-      : record.behavior.status === "trained"
+      : record.behavior.status === "trained" && missingUserActions === 0
         ? `<button onclick="setActivation('${escapeHtml(record.actuator_entity_id)}', true)">Lernen und Schalten freigeben</button>`
-        : "<p class='muted'>Freigabe wird möglich, sobald genügend Handlungen gelernt wurden.</p>";
+        : record.behavior.status === "trained"
+          ? `<p class='muted'>Freigabe noch gesperrt: ${missingUserActions} eindeutig manuelle Bedienung${missingUserActions === 1 ? "" : "en"} fehlen. Bediene das Licht dafür direkt über Home Assistant.</p>`
+          : "<p class='muted'>Freigabe wird möglich, sobald genügend Handlungen gelernt wurden.</p>";
     box.innerHTML = `
       <div class="grid-two">
         <div>
           <h3>${escapeHtml(record.actuator_entity_id)}</h3>
           <p><strong>Status:</strong> <span class="${statusClass(record)}">${escapeHtml(lifecycleLabel(record))}</span></p>
-          <p><strong>Zuordnung:</strong> automatisch</p>
+          <p><strong>Kontextzuordnung:</strong> automatisch erledigt</p>
-          <p><strong>Sicherheit:</strong> ${Math.round(record.assignment.confidence * 100)} %</p>
+          <p><strong>Zuordnungssicherheit:</strong> ${Math.round(record.assignment.confidence * 100)} %</p>
-          <p><strong>Bewertung:</strong> ${escapeHtml(record.assignment.reason)}</p>
+          <p class="muted">Dieser Wert beschreibt, wie sicher Raum, Sensoren und Zustände zu diesem Gerät passen.</p>
+          <p><strong>Ergebnis:</strong> ${escapeHtml(record.assignment.reason)}</p>
         </div>
         <div>
-          <h3>Verhaltensmodell</h3>
+          <h3>Lernfortschritt</h3>
-          <p><strong>Modus:</strong> ${escapeHtml(behaviorLabel(record))}</p>
+          <p><strong>Betriebsart:</strong> ${escapeHtml(behaviorLabel(record))}</p>
           <p><strong>Gelernte Handlungen:</strong> ${record.behavior.sample_count}</p>
           <p><strong>Davon eindeutig Benutzer:</strong> ${record.behavior.high_confidence_sample_count}</p>
+          <p><strong>Davon erkannte HA-Automationen:</strong> ${learnedAutomationActions}</p>
           <p><strong>Letztes Training:</strong> ${escapeHtml(record.behavior.last_trained_at || "noch nicht")}</p>
-          <p><strong>Status:</strong> ${escapeHtml(record.behavior.reason)}</p>
+          <p><strong>Was noch passiert:</strong> ${escapeHtml(record.behavior.reason)}</p>
           ${activationButton}
           <button class="secondary" onclick="evaluateActuator('${escapeHtml(record.actuator_entity_id)}')">Vorhersage jetzt prüfen</button>
         </div>
       </div>
-      <h3>Aktuelle Vorhersage</h3>
+      <h3>Was SillyHome aktuell vorhersagt</h3>
       ${prediction
         ? `<p><strong>${escapeHtml(prediction.target_state)}</strong> mit ${Math.round(prediction.confidence * 100)} % Sicherheit. ${escapeHtml(prediction.reason)} ${prediction.executed ? "<span class='ok'>Ausgeführt.</span>" : "<span class='muted'>Nicht ausgeführt.</span>"}</p>`
         : "<p class='muted'>Aktuell ist kein gelerntes Handlungsmuster fällig.</p>"}
-      <h3>Automatisch verwendeter Kontext</h3>
+      <h3>Welche Zusammenhänge automatisch verwendet werden</h3>
       ${evidence ? `<ul>${evidence}</ul>` : "<p class='warn'>Noch kein geeigneter Kontext erkannt. SillyHome prüft bei neuen HA-Daten erneut.</p>"}
     `;
   } catch (error) {
@@ -276,7 +321,7 @@ async function removeActuator(actuatorId) {
     await api(`v1/actuators/${encodeURIComponent(actuatorId)}`, {method: "DELETE"});
     if (currentActuatorId === actuatorId) {
       currentActuatorId = null;
-      document.getElementById("actuator-detail").textContent = "Wähle einen Aktor aus der Liste.";
+      document.getElementById("actuator-detail").textContent = "Öffne bei einem beobachteten Gerät die Details.";
     }
     await loadOverview();
   } catch (error) {

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "sillyhome-next"
-version = "0.5.0"
+version = "0.5.2"
 description = "Lokales Smart-Home-Intelligenzsystem für Home Assistant"
 requires-python = ">=3.11"
 dependencies = [

tests/actuators/test_lifecycle.py

@@ -5,6 +5,7 @@ from pathlib import Path
 
 from app.actuators.lifecycle import ActuatorReconciliationService
 from app.actuators.models import (
+    AssignmentSource,
     LifecycleStatus,
     ManualOverride,
     model_id_for_actuator,
@@ -141,7 +142,7 @@ def test_reconciliation_auto_assigns_and_trains_numeric_model(tmp_path: Path) ->
     assert "binary_sensor.abstellkammer_motion" not in artifact.supported_sensors
 
 
-def test_reconciliation_uses_best_automatic_mapping_when_ambiguous(tmp_path: Path) -> None:
+def test_reconciliation_rejects_ambiguous_numeric_mapping(tmp_path: Path) -> None:
     start = datetime(2026, 6, 1, tzinfo=timezone.utc)
     entities = [
         HaEntitySummary(
@@ -181,8 +182,62 @@ def test_reconciliation_uses_best_automatic_mapping_when_ambiguous(tmp_path: Pat
     record = service.configure_actuator("switch.garage_pump")
 
     assert record.assignment.review_required is True
-    assert record.assignment.selected_numeric_entity_id == "sensor.garage_energy"
+    assert record.assignment.selected_numeric_entity_id is None
-    assert record.lifecycle.status is LifecycleStatus.TRAINED
+    assert record.lifecycle.status is LifecycleStatus.ARCHIVED
+
+
+def test_reconciliation_does_not_cross_assign_other_room_light_energy(
+    tmp_path: Path,
+) -> None:
+    start = datetime(2026, 6, 1, tzinfo=timezone.utc)
+    entities = [
+        HaEntitySummary(
+            entity_id=(
+                "light.lichtschalter_abstellraum_"
+                "lichtschalter_abstellraum_s1"
+            ),
+            domain="light",
+            friendly_name="Licht Abstellraum",
+        ),
+        HaEntitySummary(
+            entity_id="sensor.licht_badezimmer_energy",
+            domain="sensor",
+            device_class="energy",
+            state_class="total_increasing",
+            unit_of_measurement="kWh",
+            friendly_name="Lichtschalter_Badezimmer Licht Badezimmer energy",
+        ),
+        HaEntitySummary(
+            entity_id="binary_sensor.abstellraum_ture",
+            domain="binary_sensor",
+            device_class="door",
+            friendly_name="Abstellraum Türe",
+        ),
+        HaEntitySummary(
+            entity_id="binary_sensor.briefkasten_open",
+            domain="binary_sensor",
+            device_class="opening",
+            friendly_name="Briefkasten open",
+        ),
+    ]
+    service = _service(
+        tmp_path,
+        entities,
+        {"sensor.licht_badezimmer_energy": _points(8, start, 1.0)},
+    )
+
+    record = service.configure_actuator(
+        "light.lichtschalter_abstellraum_lichtschalter_abstellraum_s1"
+    )
+
+    assert record.assignment.selected_numeric_entity_id is None
+    assert record.assignment.selected_context_entity_ids == [
+        "binary_sensor.abstellraum_ture"
+    ]
+    assert record.assignment.source is AssignmentSource.AUTOMATIC
+    assert record.assignment.confidence == 1.0
+    assert record.assignment.review_required is False
+    assert record.lifecycle.status is LifecycleStatus.ARCHIVED
 
 
 def test_legacy_manual_override_is_cleared_and_automatic_mapping_wins(tmp_path: Path) -> None:

tests/api/test_entities.py

@@ -76,6 +76,7 @@ def test_entities_returns_reader_data() -> None:
                 "entity_id": "sensor.temperature",
                 "domain": "sensor",
                 "state": None,
+                "last_changed": None,
                 "state_class": None,
             "device_class": None,
             "unit_of_measurement": None,

tests/behavior/test_engine.py

@@ -5,7 +5,7 @@ from pathlib import Path
 
 import pytest
 
-from app.actuators.models import BehaviorMode, BehaviorStatus
+from app.actuators.models import BehaviorMode, BehaviorPattern, BehaviorStatus
 from app.actuators.store import ActuatorStore
 from app.behavior.engine import BehaviorEngine, predict_behavior, service_for_state
 from app.config import Settings
@@ -189,6 +189,98 @@ def test_engine_excludes_known_automation_actions(tmp_path: Path) -> None:
     assert {pattern.source for pattern in trained.behavior.patterns} == {"user"}
 
 
+def test_engine_learns_causal_automation_for_shadow_without_user_credit(
+    tmp_path: Path,
+) -> None:
+    now = datetime.now(timezone.utc).replace(second=0, microsecond=0)
+    actuator_points: list[StateHistoryPoint] = []
+    door_points: list[StateHistoryPoint] = []
+    logbook: list[LogbookEntry] = []
+    for days_ago in (3, 2, 1):
+        action_at = now - timedelta(days=days_ago)
+        actuator_points.extend(
+            [
+                StateHistoryPoint(
+                    timestamp=action_at - timedelta(minutes=1),
+                    state="off",
+                ),
+                StateHistoryPoint(timestamp=action_at, state="on"),
+            ]
+        )
+        door_points.extend(
+            [
+                StateHistoryPoint(
+                    timestamp=action_at - timedelta(minutes=1),
+                    state="off",
+                ),
+                StateHistoryPoint(
+                    timestamp=action_at - timedelta(seconds=1),
+                    state="on",
+                ),
+            ]
+        )
+        logbook.append(
+            LogbookEntry(
+                entity_id="light.storage",
+                timestamp=action_at,
+                message="turned on",
+                context_domain="automation",
+                context_service="trigger",
+            )
+        )
+    actuator_points.sort(key=lambda point: point.timestamp)
+    door_points.sort(key=lambda point: point.timestamp)
+    settings = _settings(tmp_path)
+    store = ActuatorStore(settings.actuator_store)
+    record = store.configure("light.storage")
+    store.upsert(
+        record.model_copy(
+            update={
+                "assignment": record.assignment.model_copy(
+                    update={
+                        "selected_context_entity_ids": [
+                            "binary_sensor.storage_door"
+                        ],
+                    }
+                )
+            }
+        )
+    )
+    reader = FakeBehaviorReader(
+        entities=[],
+        history=[
+            StateHistorySeries(
+                entity_id="light.storage",
+                points=actuator_points,
+            ),
+            StateHistorySeries(
+                entity_id="binary_sensor.storage_door",
+                points=door_points,
+            ),
+        ],
+        logbook=logbook,
+    )
+    engine = BehaviorEngine(ha_reader=reader, store=store, settings=settings)
+
+    trained = engine.train("light.storage")
+    automation_patterns = [
+        pattern
+        for pattern in trained.behavior.patterns
+        if pattern.source == "automation"
+    ]
+
+    assert len(automation_patterns) == 3
+    assert trained.behavior.high_confidence_sample_count == 0
+    assert {
+        (
+            pattern.trigger_entity_id,
+            pattern.trigger_from_state,
+            pattern.trigger_to_state,
+        )
+        for pattern in automation_patterns
+    } == {("binary_sensor.storage_door", "off", "on")}
+
+
 def test_active_mode_rejects_unsafe_domains(tmp_path: Path) -> None:
     settings = _settings(tmp_path)
     store = ActuatorStore(settings.actuator_store)
@@ -259,3 +351,66 @@ def test_prediction_requires_temporal_support() -> None:
         min_support=3,
         window_minutes=30,
     ) is None
+
+
+def test_prediction_uses_fresh_causal_context_transition_outside_time_window() -> None:
+    now = datetime.now(timezone.utc).replace(second=0, microsecond=0)
+    patterns = [
+        BehaviorPattern(
+            target_state="on",
+            minute_of_day=60,
+            weekday=0,
+            context_states={"binary_sensor.storage_door": "on"},
+            trigger_entity_id="binary_sensor.storage_door",
+            trigger_from_state="off",
+            trigger_to_state="on",
+            source="automation",
+            weight=0.7,
+            observed_at=now - timedelta(days=days_ago),
+        )
+        for days_ago in (3, 2, 1)
+    ]
+
+    prediction = predict_behavior(
+        patterns,
+        current_context={"binary_sensor.storage_door": "on"},
+        current_context_changed_at={
+            "binary_sensor.storage_door": now - timedelta(seconds=10)
+        },
+        now=now,
+        min_support=3,
+        window_minutes=30,
+    )
+
+    assert prediction is not None
+    assert prediction.target_state == "on"
+    assert prediction.matching_patterns == 3
+    assert prediction.confidence == 0.7
+    assert "frischen Sensorwechsel" in prediction.reason
+
+
+def test_prediction_ignores_stale_causal_context_state() -> None:
+    now = datetime.now(timezone.utc).replace(second=0, microsecond=0)
+    pattern = BehaviorPattern(
+        target_state="on",
+        minute_of_day=60,
+        weekday=0,
+        context_states={"binary_sensor.storage_door": "on"},
+        trigger_entity_id="binary_sensor.storage_door",
+        trigger_from_state="off",
+        trigger_to_state="on",
+        source="automation",
+        weight=0.7,
+        observed_at=now - timedelta(days=1),
+    )
+
+    assert predict_behavior(
+        [pattern],
+        current_context={"binary_sensor.storage_door": "on"},
+        current_context_changed_at={
+            "binary_sensor.storage_door": now - timedelta(minutes=5)
+        },
+        now=now,
+        min_support=1,
+        window_minutes=30,
+    ) is None

tests/ha/test_ha_reader.py

@@ -15,6 +15,7 @@ class FakeHaClient(HaClient):
             {
                 "entity_id": "sensor.temperature",
                 "state": "21.5",
+                "last_changed": "2026-06-14T12:00:00+00:00",
                 "attributes": {
                     "state_class": "measurement",
                     "device_class": "temperature",
@@ -87,6 +88,7 @@ def test_ha_reader_returns_summaries() -> None:
     sensor = next(item for item in summaries if item.entity_id == "sensor.temperature")
     assert sensor.unit_of_measurement == "°C"
     assert sensor.state == "21.5"
+    assert sensor.last_changed == datetime(2026, 6, 14, 12, 0, tzinfo=timezone.utc)
     assert sensor.area_name == "Kueche"
     assert sensor.device_name == "Thermometer"
 

tests/test_addon_config.py

@@ -0,0 +1,18 @@
+from pathlib import Path
+
+
+def test_addon_does_not_expose_internal_learning_parameters() -> None:
+    config = Path("addon/config.yaml").read_text(encoding="utf-8")
+
+    assert "\noptions:" not in config
+    assert "\nschema:" not in config
+    assert "prediction_confidence" not in config
+    assert "execution_cooldown_seconds" not in config
+
+
+def test_addon_version_invalidates_application_build_layer() -> None:
+    dockerfile = Path("addon/Dockerfile").read_text(encoding="utf-8")
+
+    config_copy = dockerfile.index("COPY config.yaml /tmp/addon-config.yaml")
+    repository_clone = dockerfile.index("git clone --depth 1 --branch main")
+    assert config_copy < repository_clone

tests/test_dashboard.py

@@ -9,7 +9,14 @@ def test_dashboard_is_served_at_root() -> None:
 
     assert response.status_code == 200
     assert "SillyHome Next" in response.text
-    assert "Aktor freigeben" in response.text
+    assert "So gehst du vor" in response.text
-    assert "ausdrücklichen Freigabe pro Aktor" in response.text
+    assert "Gerät zum Lernen auswählen" in response.text
+    assert "Wie gewohnt bedienen" in response.text
+    assert "Ohne deine spätere Freigabe wird nichts geschaltet" in response.text
+    assert "Du wählst keine Sensoren und erstellst keine Regeln" in response.text
+    assert "Freigabe noch gesperrt" in response.text
+    assert "Bediene das Licht dafür direkt über Home Assistant" in response.text
+    assert "Davon erkannte HA-Automationen" in response.text
+    assert 'record.behavior.status === "trained" && missingUserActions === 0' in response.text
     assert "Automation-Entwurf" not in response.text
     assert "Manuelle Overrides" not in response.text