Abductive Reasoning with Recurrent Neural Networks
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.
A. M. Abdelbar et al., "Abductive Reasoning with Recurrent Neural Networks," Neural Networks, Elsevier, Jun 2003.
The definitive version is available at http://dx.doi.org/10.1016/S0893-6080(03)00114-X
Electrical and Computer Engineering
Keywords and Phrases
Abductive; Neural Networks; Reasoning
Article - Journal
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