---
title: "Belief Change Based on Global Minimisation"
authors:
  - "James P. Delgrande"
  - "Jérôme Lang"
  - "Torsten Schaub"
doi: ""
url: "https://ijcai.org/proceedings/2007/397"
pdf: "https://ijcai.org/proceedings/2007/0397.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "2468-2472"
track: ""
keywords:
---

# Belief Change Based on Global Minimisation

James P. Delgrande, Jérôme Lang, Torsten Schaub

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 2468-2472.

## Abstract

A general framework for minimisation-based belief change is presented. A problem instance is made up of an undirected graph, where a formula is associated with each vertex. For example, vertices may represent spatial locations, points in time, or some other notion of locality. Information is shared between vertices via a process of minimisation over the graph. We give equivalent semantic and syntactic characterisations of this minimisation. We also show that this approach is general enough to capture existing minimisation-based approaches to belief merging, belief revision, and (temporal) extrapolation operators. While we focus on a set-theoretic notion of minimisation, we also consider other approaches, such as cardinality-based and priority-based minimisation.