Abductive reasoning with recurrent neural networks.

Abstract

Abduction is the process of proceeding from data describing a set of observations or events, to a set of hypotheses which best explains or accounts for the data. Cost-based abduction (CBA) is a formalism in which evidence to be explained is treated as a goal to be proven, proofs have costs based on how much needs to be assumed to complete the proof, and the set of assumptions needed to complete the least-cost proof are taken as the best explanation for the given evidence. In previous work, we presented a method for using high order recurrent networks to find least cost proofs for CBA instances. Here, we present a method that significantly reduces the size of the neural network that is produced for a given CBA instance. We present experimental results describing the performance of this method and comparing its performance to that of the previous method.