Distributed computation for neural-based abductive reasoning

This work extends a recent model for neural-based abductive reasoning to account for the monotonic class. A problem is said to be monotonic some causes, together, explain the same effect. For this, we developed a new computational principle, called the softmin, and implemented it within a neural architecture. Simulation results are very satisfactory and should stimulate future research.