Build a Neuro-Symbolic Agent for IoT Anomaly Response
Addressing the challenge of reasoning under perceptual uncertainty in real-world applications like IoT device operation, this challenge focuses on developing a neuro-symbolic agent. The agent will integrate the natural language understanding and high-level planning capabilities of an agent with a symbolic reasoning system. Its primary function will be to detect anomalies in simulated IoT sensor data streams and generate adaptive, context-aware responses to maintain system stability, akin to optimizing power distribution system restoration. Participants will need to design how continuous, often noisy, sensor data is translated into discrete symbolic facts, which then inform a symbolic planner. this will be crucial for enforcing schema-driven validation and ensuring the logical consistency and safety of the LLM's generated plans and actions. The goal is a robust system that can handle complex scenarios where a purely neural or symbolic approach might fall short, bridging the gap between continuous perception and discrete symbolic planning to enable intelligent, adaptive control.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a neuro-symbolic agent leveraging Vicuna-33B (via Langroid) to detect IoT anomalies, translate perceptual data to symbolic facts, and execute adaptive responses.
How work is evaluated
The evaluation will assess the neuro-symbolic agent's ability to accurately detect IoT anomalies and execute appropriate, safe, and context-aware responses in a simulated environment.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Build a simulated IoT environment for testing anomaly detection and response.
Design a neuro-symbolic architecture integrating neural and symbolic components.
Implement mechanisms to translate uncertain perceptual data into discrete symbolic facts.
Utilize Marvin for schema-driven validation and constraint enforcement in agent plans.
Develop a symbolic planning component for adaptive IoT control.
Reference links and supporting material
Sample data for 1 tasks
How this agent runs
The evaluation will assess the neuro-symbolic agent's ability to accurately detect IoT anomalies and execute appropriate, safe, and context-aware responses in a simulated environment.
Challenge input
{ "sensor_data_stream": [{"timestamp": "datetime", "sensor_id": "string", "value": "float"}], "anomaly_type": "string" }
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{ "anomaly_detected": "boolean", "detected_anomaly_type": "string", "explanation": "string", "action_sequence": [{"device_id": "string", "command":...
- Verifies that the agent correctly identifies the embedded anomaly type.
- Checks if the `adherence_to_safety_rules` flag is true, indicating no safety violations.
- Ensures the generated action sequence is non-empty and contains valid commands for simulated devices.
- ResponseTime target: 2
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- The evaluation module defines the checks.
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.