Generalizing Fuzzy Logic Probabilistic Inferences
This work provides a theoretical extension for probabilistic inference in fuzzy logic, but appears incremental as it builds directly on Boole's established constraints.
The paper tackles the problem of computing probabilities for events defined by arbitrary propositional formulas by showing that linear representations of a subclass of boolean symmetric functions generalize Boole's linear constraints on probabilities.
Linear representations for a subclass of boolean symmetric functions selected by a parity condition are shown to constitute a generalization of the linear constraints on probabilities introduced by Boole. These linear constraints are necessary to compute probabilities of events with relations between the. arbitrarily specified with propositional calculus boolean formulas.