Automatic Derivation Of Formulas Using Reforcement Learning
  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.
