Automatic Derivation Of Formulas Using Reforcement Learning
This addresses the need for automated formula derivation in scientific computing, but it appears incremental as it builds on existing reinforcement learning and tree-based methods.
The paper tackles the problem of automatically deriving formulas across scientific disciplines by representing formulas as multiway trees and using Q-learning in a feature space to guide derivation steps, with an example application in nuclear reactor physics.
This paper presents an artificial intelligence algorithm that can be used to derive formulas from various scientific disciplines called automatic derivation machine. First, the formula is abstractly expressed as a multiway tree model, and then each step of the formula derivation transformation is abstracted as a mapping of multiway trees. Derivation steps similar can be expressed as a reusable formula template by a multiway tree map. After that, the formula multiway tree is eigen-encoded to feature vectors construct the feature space of formulas, the Q-learning model using in this feature space can achieve the derivation by making training data from derivation process. Finally, an automatic formula derivation machine is made to choose the next derivation step based on the current state and object. We also make an example about the nuclear reactor physics problem to show how the automatic derivation machine works.