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Symbolic Regression (SR) can now be enhanced with expert knowledge using the Symbolic Q-network. This interactive framework improves equation discovery, outperforming standard models in real-world applications.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Scientific Discovery

Background:

  • Symbolic Regression (SR) aims to find mathematical expressions from data.
  • Existing SR methods struggle with large search spaces and integrating domain knowledge.
  • Transformer models in SR often lack expert interaction capabilities.

Purpose of the Study:

  • To introduce the Symbolic Q-network, an interactive framework for large-scale SR.
  • To leverage reinforcement learning and expert co-design for improved equation discovery.
  • To overcome limitations of existing transformer-based SR approaches.

Main Methods:

  • Developed the Symbolic Q-network, a novel interactive SR framework.
  • Utilized reinforcement learning without a transformer-based decoder.
  • Implemented a co-design mechanism for seamless expert-in-the-loop integration.

Main Results:

  • Symbolic Q-network demonstrated performance comparable to pre-trained models on benchmarks.
  • The interactive co-design significantly boosted performance on real-world problems.
  • Achieved superior performance gains compared to standard autoregressive models.

Conclusions:

  • The Symbolic Q-network offers an effective interactive approach to SR.
  • Integrating domain expert knowledge via co-design is crucial for SR success.
  • This framework advances the field of automated scientific discovery.