AN EFFECTIVE NEURAL MODEL MECHANIZING HARD CAUSAL REASONING PROBLEMS WITH WTA and WTO NEURAL COMPUTATIONS
LOTFI BEN ROMDHANE, B. Ayeb · International Journal of Artificial Intelligence Tools · 2004
In this work, we develop a neural model to solve causal reasoning problems (said also abduction) in the open, independent and incompatibility classes. We model the reasoning process by a single and global energy function using cooperative and competitive neural computation. The update rules of the distinct connections of the network are derived from its energy function using gradient descent techniques. Simulation results reveal a good performance of the model.