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 (CKA) 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 introduce 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 on a 300-hypothesis, 900-rule particularly-difficult instance of CBA.
D. C. Wunsch et al., "Negative Reinforcement and Backtrack-Points for Recurrent Neural Networks for Cost-Based Abduction," Proceedings of the IEEE International Joint Conference on Neural Networks, 2005, Institute of Electrical and Electronics Engineers (IEEE), Jan 2005.
The definitive version is available at https://doi.org/10.1109/IJCNN.2005.1555959
IEEE International Joint Conference on Neural Networks, 2005
Electrical and Computer Engineering
Keywords and Phrases
Backtrack-Points; Cost-Based Abduction; Heuristic Perturbations; High Order Recurrent Neural Networks; Learning (Artificial Intelligence); Negative Reinforcement; Perturbation Techniques; Recurrent Neural Nets
Article - Conference proceedings
© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jan 2005