  Belief change is a fundamental problem in AI: Agents constantly have to
update their beliefs to accommodate new observations. In recent years, there
has been much work on axiomatic characterizations of belief change. We claim
that a better understanding of belief change can be gained from examining
appropriate semantic models. In this paper we propose a general framework in
which to model belief change. We begin by defining belief in terms of knowledge
and plausibility: an agent believes p if he knows that p is more plausible than
its negation. We then consider some properties defining the interaction between
knowledge and plausibility, and show how these properties affect the properties
of belief. In particular, we show that by assuming two of the most natural
properties, belief becomes a KD45 operator. Finally, we add time to the
picture. This gives us a framework in which we can talk about knowledge,
plausibility (and hence belief), and time, which extends the framework of
Halpern and Fagin for modeling knowledge in multi-agent systems. We then
examine the problem of ``minimal change''. This notion can be captured by using
prior plausibilities, an analogue to prior probabilities, which can be updated
by ``conditioning''. We show by example that conditioning on a plausibility
measure can capture many scenarios of interest. In a companion paper, we show
how the two best-studied scenarios of belief change, belief revisionand belief
update, fit into our framework.
