title: A softmin-based neural model for causal reasoning.
abstract: This paper extends a neural model for causal reasoning to mechanize the monotonic class. Hence, the resulting model is able to solve multiple, varied causal problems in the open, independent, incompatibility and monotonic classes. First, additivity between causes is formalized as a fuzzy AND-ing process. Second, an activation mechanism called the "softmin" is developed to solve additive interactions. Third, the softmin is implemented within a neural architecture. Experimental results on real-world and artificial problems reveal a good performance of the model and should stimulate future research.
authors.affiliation: Department of Computer Science, University of Sherbrooke, QC, Canada. Lotfi.Ben.Romdhane@Usherbrooke.ca
authors.forename: Lotfi
authors.initials: L
authors.lastname: Ben Romdhane
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D015984:Causality; D003198:Computer Simulation; D003661:Decision Support Techniques; D017143:Fuzzy Logic; D016015:Logistic Models; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*