FAQ & Troubleshooting

This section addresses frequently asked questions (FAQs) about Project KARL and provides guidance for troubleshooting common issues that developers or users might encounter during integration or usage.

Frequently Asked Questions (FAQs)

Troubleshooting Common Issues

• Issue: Build Failure - "Unresolved reference" to KARL core types (LearningEngine, InteractionData, etc.) in implementation modules (e.g., :karl-kldl, :karl-room).
  Cause: Gradle dependency misconfiguration. The implementation module is likely not correctly depending on :karl-core, or :karl-core is not correctly exposing its common artifacts for its JVM target.
  Solution:

  1. Ensure :karl-core/build.gradle.kts defines a jvm() target in its kotlin { ... } block.
  2. Ensure :karl-core's commonMain dependencies (like kotlinx-coroutines-core, if its API uses coroutine types) are declared with api(...) if they need to be transitive.
  3. In the dependent module (e.g., :karl-kldl), ensure its commonMain dependencies include api(project(":karl-core")).
  4. Perform a thorough Gradle clean (./gradlew clean, potentially delete .gradle and .idea folders) and re-sync/rebuild.
  5. Verify all import statements in your Kotlin files are correct.

  Refer to the dependency setup guide↗ and ensure your module configurations align.

• Issue: Build Failure - KSP errors in :karl-room (e.g., "MissingType", "Unresolved reference: kspJvm").
  Cause: Incorrect KSP plugin setup, version mismatch, or KSP not seeing types from :karl-core.
  Solution:

  1. Ensure the com.google.devtools.ksp plugin is applied in :karl-room/build.gradle.kts and its version (in settings.gradle.kts or libs.versions.toml) matches your Kotlin version (e.g., Kotlin 1.9.23 uses KSP 1.9.23-1.0.19).
  2. Ensure androidx.room:room-compiler is added as a KSP dependency to the correct target configuration (e.g., kspJvm("androidx.room:room-compiler:VERSION") within jvmMain.dependencies).
  3. Ensure :karl-room's commonMain has api(project(":karl-core")) and api("androidx.room:room-common:VERSION").
  4. Verify all Room-annotated classes (@Entity, @Dao, @Database, @TypeConverter) are correctly defined within the :karl-room module and have correct imports.

  Review the Room setup↗in the Getting Started guide or example project.

• Issue: KARL is not making any predictions or predictions seem random.
  Cause: Insufficient learning data, incorrect InteractionData format/features, issues in the LearningEngine model or training logic, or the DataSource not emitting data correctly.
  Solution:

  1. Verify your DataSource is correctly implemented and successfully sending InteractionData to the KarlContainer (use print statements or logging in your DataSource and KLDLLearningEngine.trainStep() for debugging).
  2. Ensure the InteractionData's type and details map contain meaningful features relevant to the prediction task.
  3. Check the console logs from your LearningEngine implementation during trainStep() for any errors or warnings.
  4. The AI needs time and sufficient diverse interactions to learn. Initial predictions will be naive.
  5. Consider the complexity of the task vs. the simplicity of the default model (e.g., the simple MLP in :karl-kldl). More complex patterns may require a more sophisticated model architecture.

• Issue: Application performance degrades after integrating KARL.
  Cause: Learning steps (trainStep) or predictions (getPrediction) might be too computationally intensive for the device or running on the main UI thread.
  Solution:

  1. Ensure all calls to KarlContainer methods that perform significant work (initialize, saveState, getPrediction, and the internal trainStep triggered by DataSource) are executed on background threads. KARL is designed with suspend functions and returns Jobs to facilitate this, using the CoroutineScope you provide.
  2. Profile your LearningEngine's trainStep and predict methods.
  3. Optimize feature extraction logic within your LearningEngine.
  4. Consider a simpler model architecture if the current one is too heavy.
  5. For trainStep, the LearningEngine implementation might need to queue updates or perform them less frequently if individual steps are too costly.

• Issue: Saved AI state (KarlContainerState) doesn't seem to load correctly after app restart.
  Cause: Problems in the DataStorage implementation's save/load logic, issues with state serialization/deserialization in the LearningEngine, or database schema/version mismatches (if using Room/SQLite).
  Solution:

  1. Verify that karlContainer.saveState() is being called reliably before the application closes or the container is released.
  2. Debug the saveContainerState and loadContainerState methods in your DataStorage implementation. Check for any I/O errors or exceptions.
  3. Critically review the serialization (e.g., in LearningEngine.getCurrentState()) and deserialization (in LearningEngine.initialize()) logic for the model's state. Ensure it's robust.
  4. If using Room, check for database migration issues if you've changed your entity schemas. Ensure exportSchema = true is set and you handle migrations correctly.

Performance Considerations and Tips

• Asynchronous Operations: Always use the provided CoroutineScope and launch KARL operations (especially trainStep via DataSource, initialize, saveState, getPrediction) on appropriate background dispatchers (e.g., Dispatchers.IO for disk/DB, Dispatchers.Default for CPU-intensive model updates).

• Efficient Feature Extraction: The process of converting InteractionData into numerical features for your model should be as efficient as possible, as it runs frequently.

• Model Complexity: Balance model complexity with on-device resource constraints (CPU, memory). Simpler models (like MLPs) train and predict faster but might capture less complex patterns. More complex models (RNNs, Transformers) are more powerful but more demanding.

• trainStep Frequency: If individual training steps are computationally noticeable, your LearningEngine might need internal logic to batch updates or train less frequently (e.g., after N interactions or on an idle timer) rather than on every single interaction.

• State Serialization: Efficient serialization/deserialization of KarlContainerState is important for quick app startup (loading state) and shutdown (saving state). Choose an efficient format (e.g., Protobuf, FlatBuffers, or optimized Keras model saving if using KotlinDL) over very verbose ones like uncompressed JSON for large model states.

• Profile: Use profiling tools (IntelliJ IDEA profiler, Android Studio profiler if targeting Android) to identify performance bottlenecks within your KARL integration or specific engine/storage implementations.

Debugging the Learning or Prediction Process

• Logging: Add extensive logging (e.g., using a simple println for development, or a proper logging library like SLF4J/Logback for JVM) within:

  • Your DataSource implementation (to see what InteractionData is being sent).
  • Your LearningEngine's trainStep and predict methods (to see input features, model outputs, confidence scores).
  • Your DataStorage implementation (to verify saving and loading).

• Inspect Stored State: If using SQLite (via Room or SQLDelight), use a database browser tool to inspect the contents of the local database files to see what InteractionData or KarlContainerState is being stored.

• Simplified Test Cases: Create minimal test scenarios with very predictable sequences of InteractionData to verify if the model is learning basic patterns as expected.

• Model Summary: If using KotlinDL, use model.summary() to print the architecture of your neural network and ensure it's configured as intended.

• Isolate Components: Test each KARL component (LearningEngine, DataStorage) with mock inputs/dependencies in unit tests to verify their individual logic before integrating them into the full KarlContainer.