Abstract
Discovering tractable mathematical expressions that best explain a dataset is a long-standing challenge in artificial intelligence.
This problem, known as symbolic regression, is relevant when one seeks to generate new physical knowledge and insights.
Since practitioners are primarily interested in knowledge generation, the ability to interact with a symbolic regression algorithm would be highly valuable.
Thus, we present an interactive symbolic regression framework that allows users not only to configure runs, but also to control the system during training.
The interface provides real-time visualization and diagnostics to help guide the user as they control the algorithm on the fly.
Keywords
- Machine Learning: general
- Knowledge Representation and Reasoning: general
