Iterated Belief Base Revision: A Dynamic Epistemic Logic Approach
This work addresses foundational issues in belief change theory for AI and logic communities, but it is incremental as it builds on existing frameworks without broad practical impact.
The paper tackles the problem of characterizing iterated belief revision postulates using priority graphs and dynamic epistemic logic, providing syntactic representations of belief change operators and new negative results on representing these operations with graph transformations.
AGM's belief revision is one of the main paradigms in the study of belief change operations. In this context, belief bases (prioritised bases) have been largely used to specify the agent's belief state - whether representing the agent's `explicit beliefs' or as a computational model for her belief state. While the connection of iterated AGM-like operations and their encoding in dynamic epistemic logics have been studied before, few works considered how well-known postulates from iterated belief revision theory can be characterised by means of belief bases and their counterpart in a dynamic epistemic logic. This work investigates how priority graphs, a syntactic representation of preference relations deeply connected to prioritised bases, can be used to characterise belief change operators, focusing on well-known postulates of Iterated Belief Change. We provide syntactic representations of belief change operators in a dynamic context, as well as new negative results regarding the possibility of representing an iterated belief revision operation using transformations on priority graphs.