Rules, Belief Functions and Default Logic
This work provides a theoretical foundation for rule-based systems in AI, but it is incremental as it builds on existing belief function and default logic theories.
The paper introduces a belief function framework for representing various types of rules, including numerical and default rules, and shows how it generalizes to other logics and relates to Reiter's Default Logic as a limiting case.
This paper describes a natural framework for rules, based on belief functions, which includes a repre- sentation of numerical rules, default rules and rules allowing and rules not allowing contraposition. In particular it justifies the use of the Dempster-Shafer Theory for representing a particular class of rules, Belief calculated being a lower probability given certain independence assumptions on an underlying space. It shows how a belief function framework can be generalised to other logics, including a general Monte-Carlo algorithm for calculating belief, and how a version of Reiter's Default Logic can be seen as a limiting case of a belief function formalism.