Iterated Belief Change as Learning

Nicolas Schwind, Katsumi Inoue, Sébastien Konieczny, Pierre Marquis

Abstract: In this work, we show how the class of improvement operators --- a general class of iterated belief change operators --- can be used to define a learning model. Focusing on binary classification, we present learning and inference algorithms suited to this learning model and we evaluate them empirically. Our findings highlight two key insights: first, that iterated belief change can be viewed as an effective form of online learning, and second, that the well-established axiomatic foundations of belief change operators offer a promising avenue for the axiomatic study of classification tasks.

External IDs:dblp:conf/ijcai/SchwindIKM25