Ranking functions for belief change

Hunter, Aaron (Aaron Hunter (Aaron_Hunter)) (author)
(editor)
(translator)

© 2014 SciTePress

Proceedings of the 6th International Conference on Agents and Artificial Intelligence in Angers, France, 2014. In this paper, we explore the use of ranking functions in reasoning about belief change. It is well-known that the semantics of belief revision can be defined either through total pre-orders or through ranking functions over states. While both approaches have similar expressive power with respect to single-shot belief revision, we argue that ranking functions provide distinct advantages at both the theoretical level and the practical level, particularly when actions are introduced. We demonstrate that belief revision induces a natural algebra over ranking functions, which treats belief states and observations in the same manner. When we introduce belief progression due to actions, we show that many natural domains can be easily represented with suitable ranking functions. Our formal framework uses ranking functions to represent belief revision and belief progression in a uniform manner; we demonstrate the power of our approach through formal results, as well as a series of natural problems in commonsense reasoning.

https://dl.acm.org/citation.cfm?id=2970622