Belief Functions and Default Reasoning

TL;DR

This paper introduces a novel approach to default reasoning using belief functions, employing epsilon-belief assignments to effectively handle issues like specificity and ambiguity in non-monotonic logic.

Contribution

It proposes a new belief function-based framework for default reasoning, addressing key problems in non-monotonic systems with two related semantic systems.

Findings

Addresses specificity, irrelevance, and ambiguity issues.

Relates the new systems to existing non-monotonic frameworks.

Demonstrates the second system's effectiveness in resolving classical problems.

Abstract

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.