Reviews: Bridging Machine Learning and Logical Reasoning by Abductive Learning

Still, if you can do some version of the Mayan hieroglyphics, or work that example into the introduction, it would improve the paper even more. They restrict themselves to classification problems, i.e., a mapping from perceptual input to {0,1}; the discrete symbols output by the perception model act as latent variables sitting in between the input and the binary decision. Their approach is to alternate between (1) inferring a logic program consistent with the training examples, conditioned on the output of the perception model, and (2) training the perception model to predict the latent discrete symbols. Because the perception model may be unreliable, particularly early on in training, the logic program is allowed to revise or abduce the outputs of perception. The problem they pose -- integrating learned perception with learned symbolic reasoning -- is eminently important.