Belief Expansion in Subset Models

TL;DR

This paper introduces a belief expansion operator within subset models for justification logic, analyzing its properties and differences from previous symbolic approaches, thus advancing the semantics of evidence-based belief systems.

Contribution

It proposes a new belief expansion operator for subset models and compares its properties to existing symbolic methods in justification logic.

Findings

The belief expansion operator satisfies key logical properties.

Differences between subset model semantics and symbolic approaches are characterized.

The new operator enhances the understanding of evidence accumulation in justification logic.

Abstract

Subset models provide a new semantics for justifcation logic. The main idea of subset models is that evidence terms are interpreted as sets of possible worlds. A term then justifies a formula if that formula is true in each world of the interpretation of the term. In this paper, we introduce a belief expansion operator for subset models. We study the main properties of the resulting logic as well as the differences to a previous (symbolic) approach to belief expansion in justification logic.