Belief Functions and Default Reasoning
This work addresses challenges in non-monotonic reasoning for AI and logic, but appears incremental as it builds on existing theories like Adams' epsilon-semantics.
The paper tackles the problem of default reasoning by proposing a new approach based on belief functions, using epsilon-belief assignments to handle default information, and shows that their second system correctly addresses key issues like specificity and ambiguity.
We present a new approach to dealing with default information based on the theory of belief functions. Our semantic structures, inspired by Adams' epsilon-semantics, are epsilon-belief assignments, where values committed to focal elements are either close to 0 or close to 1. We define two systems based on these structures, and relate them to other non-monotonic systems presented in the literature. We show that our second system correctly addresses the well-known problems of specificity, irrelevance, blocking of inheritance, ambiguity, and redundancy.