Top-Down Neural Model For Formulae

Karel Chvalovský

Abstract: We present a simple neural model that given a formula and a property tries to answer the question whether the formula has the given property, for example whether a propositional formula is always true. The structure of the formula is captured by a feedforward neural network recursively built for the given formula in a top-down manner. The results of this network are then processed by two recurrent neural networks. One of the interesting aspects of our model is how propositional atoms are treated. For example, the model is insensitive to their names, it only matters whether they are the same or distinct.

Keywords: logic, formula, recursive neural networks, recurrent neural networks

TL;DR: A top-down approach how to recursively represent propositional formulae by neural networks is presented.