Neural-Logic Belief Networks-A tool for knowledge representation and reasoning

The author sketches a new architecture for representing knowledge and performing commonsense reasoning. It is an acyclical directed graph formalism with a neural network computation model and a Prolog-style unification mechanism called Neural-Logic Belief Network. In this representation, a concept is either believed, its negation is believed, unknown, or in the state of contradiction. Each proposition also has a degree-of-belief value to represent its reliability and/or certainty. Every directed link carries a tuple of real numbers to model a three-valued logic and other relations such as the commonsense IF-THEN rules. Due to the nature of network computation, it has an extreme level of tolerance to contradictory input knowledge.