Recurrent Neural Networks with Backtrack-points and Negative Reinforcement Applied to Cost-based Abduction
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 an AI 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 this paper, we present two techniques for improving the performance of high order recurrent networks (HORN) applied to cost-based abduction. In the backtrack-points technique, we use heuristics to recognize early that the network trajectory is moving in the wrong direction; we then restore the network state to a previously stored point, and apply heuristic perturbations to nudge the network trajectory in a different direction. In the negative reinforcement technique, we add hyperedges to the network to reduce the attractiveness of local minima. We apply these techniques to a suite of six large CBA instances, systematically generated to be difficult.
A. M. Abdelbar et al., "Recurrent Neural Networks with Backtrack-points and Negative Reinforcement Applied to Cost-based Abduction," Neural Networks, vol. 18, no. 5-6, pp. 755-761, Elsevier, Jan 2005.
The definitive version is available at https://doi.org/10.1016/j.neunet.2005.06.026
2005 International Joint Conference on Neural Networks (IJCNN) (2005: Jul. 31-Aug. 4, Montreal, Canada)
Electrical and Computer Engineering
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 2005 Elsevier, All rights reserved.
01 Jan 2005