Reinforcement Learning with Analogical Similarity to Guide Schema
  Induction and Attention
  Research in analogical reasoning suggests that higher-order cognitive
functions such as abstract reasoning, far transfer, and creativity are founded
on recognizing structural similarities among relational systems. Here we
integrate theories of analogy with the computational framework of reinforcement
learning (RL). We propose a psychology theory that is a computational synergy
between analogy and RL, in which analogical comparison provides the RL learning
algorithm with a measure of relational similarity, and RL provides feedback
signals that can drive analogical learning. Simulation results support the
power of this approach.
