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Perception and reasoning are basic human abilities that are seamlesslyconnected as part of human intelligence. However, in current machine learningsystems, the perception and reasoning modules are incompatible. Tasks requiringjoint perception and reasoning ability are difficult to accomplish autonomouslyand still demand human intervention. Inspired by the way language expertsdecoded Mayan scripts by joining two abilities in an abductive manner, thispaper proposes the abductive learning framework. The framework learnsperception and reasoning simultaneously with the help of a trial-and-errorabductive process. We present the Neural-Logical Machine as an implementationof this novel learning framework. We demonstrate that--using human-likeabductive learning--the machine learns from a small set of simple hand-writtenequations and then generalizes well to complex equations, a feat that is beyondthe capability of state-of-the-art neural network models. The abductivelearning framework explores a new direction for approaching human-levellearning ability.

Tunneling Neural Perception and Logic Reasoning through Abductive Learning