  The study of belief change has been an active area in philosophy and AI. In
recent years two special cases of belief change, belief revision and belief
update, have been studied in detail. In a companion paper, we introduce a new
framework to model belief change. This framework combines temporal and
epistemic modalities with a notion of plausibility, allowing us to examine the
change of beliefs over time. In this paper, we show how belief revision and
belief update can be captured in our framework. This allows us to compare the
assumptions made by each method, and to better understand the principles
underlying them. In particular, it shows that Katsuno and Mendelzon's notion of
belief update depends on several strong assumptions that may limit its
applicability in artificial intelligence. Finally, our analysis allow us to
identify a notion of minimal change that underlies a broad range of belief
change operations including revision and update.

Artificial Intelligence;Logic in Computer Science