A Neural Approach to Abductive Multi-adjoint Reasoning
AIMSA '02 Proceedings of the 10th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
A Distributed Artificial Network Solving Complex and Multiple Causal Associations
Applied Intelligence
A Potts spin MFT network solving multiple causal interactions
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Neural network models for abduction problems solving
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
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In the last decade, abduction has been a very active research area. This has resulted in a variety of models mechanizing abduction, namely within a probabilistic or logical framework. Recently, a few abductive models have been proposed within a neural framework. Unfortunately, these neural/probablistic/logical-based models cannot address complex abduction problems. In this paper, we propose a new extended neural-based model to deal with abduction problems which could be monotonic, open, and incompatible