On Computational Aspects of Iterated Belief Change
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 1770-1776. https://doi.org/10.24963/ijcai.2020/245
Iterated belief change aims to determine how the belief state of a rational agent evolves given a sequence of change formulae. Several families of iterated belief change operators (revision operators, improvement operators) have been pointed out so far, and characterized from an axiomatic point of view. This paper focuses on the inference problem for iterated belief change, when belief states are represented as a special kind of stratified belief bases. The computational complexity of the inference problem is identified and shown to be identical for all revision operators satisfying Darwiche and Pearl's (R*1-R*6) postulates. In addition, some complexity bounds for the inference problem are provided for the family of soft improvement operators. We also show that a revised belief state can be computed in a reasonable time for large-sized instances using SAT-based algorithms, and we report empirical results showing the feasibility of iterated belief change for bases of significant sizes.
Knowledge Representation and Reasoning: Belief Change, Belief Merging
Knowledge Representation and Reasoning: Non-monotonic Reasoning, Common-Sense Reasoning