Source-Sensitive Belief Change
This work provides a unified approach to belief revision for AI and logic communities, but it appears incremental as it builds directly on existing AGM extensions.
The authors tackled the limitations of the AGM model for belief revision by proposing a new framework that addresses paraconsistent, multi-agent, and non-prioritized extensions, and they analyzed its features and the satisfiability of AGM postulates.
The AGM model is the most remarkable framework for modeling belief revision. However, it is not perfect in all aspects. Paraconsistent belief revision, multi-agent belief revision and non-prioritized belief revision are three different extensions to AGM to address three important criticisms applied to it. In this article, we propose a framework based on AGM that takes a position in each of these categories. Also, we discuss some features of our framework and study the satisfiability of AGM postulates in this new context.