Nova AI Research Agents

2.DataNova AI Research Agents

2.1 Implementation Logic and Technical Scheme of AI Agents

Core Functions of AI Agents:

• Autonomous Research and Experiment Design:

  • Methodology: Utilizes Reinforcement Learning (RL), particularly Deep Q-Network (DQN) and Policy Gradient Methods like Proximal Policy Optimization (PPO), to simulate and optimize experimental processes. AI agents learn from historical experiment data, predict results, and dynamically adjust experiment parameters to enhance scientific outcomes.

  • Implementation Process:

    • Data Input: Convert experiment objectives into vector features, including experimental conditions, expected outcomes, etc.

    • Model Training: Use pre-trained models or train from scratch, leveraging transfer learning to speed up the learning process.

    • Experiment Design: AI designs experiments through simulated environments (like gym environments), adjusting parameters to maximize certain scientific indicators.

  • Technical Architecture:

    • Deep Learning Frameworks: Such as TensorFlow or PyTorch for model training and prediction.

    • Simulation Environment: Using frameworks like OpenAI Gym to create simulated research experiment environments.

  • Sample Code (for conceptual illustration, actual implementation might be more complex):

    import gym
    from stable_baselines3 import PPO
    
    # Assuming there's a gym environment named ScienceExperiment
    env = gym.make('ScienceExperiment-v0')
    
    model = PPO('MlpPolicy', env, verbose=1)
    model.learn(total_timesteps=100000)
    
    # Use the model for experiment design
    obs = env.reset()
    for i in range(1000):
        action, _states = model.predict(obs, deterministic=True)
        obs, rewards, done, info = env.step(action)
        if done:
          obs = env.reset()

• Research Question Answering and Intelligent Reasoning:

  • Methodology: Employs Natural Language Processing (NLP) with Transformer models (like BERT, GPT series) at the core to understand and answer research questions. Additionally, uses Knowledge Graphs and Graph Neural Networks (GNN) for reasoning.

  • Implementation Process:

    • Question Understanding: Semantic encoding of questions using BERT or its variants.

    • Knowledge Retrieval: Extract relevant information from the knowledge graph, apply GNN for context reasoning.

    • Answer Generation: Use generative models like GPT-3 or its optimizations to produce natural language answers.

  • Technical Architecture:

    • NLP Tools: Hugging Face Transformers library for model application.

    • Knowledge Graph: Neo4j or similar graph databases for storing scientific knowledge.

    • Reasoning System: Combines SPARQL query language with GNN for knowledge reasoning.

• Data Quality Assessment and Content Optimization:

  • Methodology: Based on Supervised Learning, using Feature Engineering and Model Interpretability Techniques like SHAP (SHapley Additive exPlanations) to assess data quality. AI agents learn to extract features from datasets to evaluate accuracy, completeness, and innovativeness.

  • Implementation Process:

    • Feature Extraction: Preprocess data to extract key features.

    • Model Training: Train multi-task learning models to evaluate data quality across multiple dimensions.

    • Feedback Mechanism: Provide improvement suggestions or data augmentation strategies.

  • Technical Architecture:

    • Machine Learning Frameworks: Use Scikit-learn or PyTorch for feature processing and model training.

    • Interpretability Tools: Employ SHAP or LIME libraries to explain model decisions.

• Intelligent Iteration and Automatic Learning:

  • Methodology: Adopt Online Learning and Incremental Learning strategies to ensure AI agents can continue learning from new data, not just static datasets.

  • Implementation Process:

    • Data Stream Processing: Real-time processing of new data, updating model weights.

    • Incremental Learning Models: Like Online Gradient Descent for continuous model updates.

  • Technical Architecture:

    • Online Learning Libraries: Such as River or Creme for implementing incremental learning algorithms.

2.2 Token Incentives and AI Service Charges

• AI Service Fee: Calculated based on used computational resources, experimental complexity, and time.

• AI Service Fee = ( Computational Resource Factor × Experiment Complexity Factor ) + ( Time Factor × Resource Utilization Rate )