Tunneling Neural Perception and Logic Reasoning through Abductive
  Learning
  Perception and reasoning are basic human abilities that are seamlessly
connected as part of human intelligence. However, in current machine learning
systems, the perception and reasoning modules are incompatible. Tasks requiring
joint perception and reasoning ability are difficult to accomplish autonomously
and still demand human intervention. Inspired by the way language experts
decoded Mayan scripts by joining two abilities in an abductive manner, this
paper proposes the abductive learning framework. The framework learns
perception and reasoning simultaneously with the help of a trial-and-error
abductive process. We present the Neural-Logical Machine as an implementation
of this novel learning framework. We demonstrate that--using human-like
abductive learning--the machine learns from a small set of simple hand-written
equations and then generalizes well to complex equations, a feat that is beyond
the capability of state-of-the-art neural network models. The abductive
learning framework explores a new direction for approaching human-level
learning ability.
