MODELING BELIEF IN DYNAMIC-SYSTEMS .1. FOUNDATIONS

Belief change is a fundamental problem in AI: Agents constantly have t o update their beliefs to accommodate new observations. In recent year s, there has been much work on axiomatic characterizations of belief c hange. We claim that a better understanding of belief change can be ga ined from examining appropriate semantic models. In this paper we prop ose a general framework in which to model belief change. We begin by d efining belief in terms of knowledge and plausibility: an agent believ es phi if he knows that phi is more plausible than -phi. We then consi der some properties defining the interaction between knowledge and pla usibility, and show how these properties affect the properties of beli ef. In particular, we show that by assuming two of the most natural pr operties, belief becomes a KD45 operator. Finally, we add time to the picture. This gives us a framework in which we can talk about knowledg e, plausibility (and hence belief), and time, which extends the framew ork of Halpern and Fagin for modeling knowledge in multi-agent systems . We then examine the problem of ''minimal change''. This notion can b e captured by using prior plausibilities, an analogue to prior probabi lities, which can be updated by ''conditioning''. We show by example t hat conditioning on a plausibility measure can capture many scenarios of interest. In a companion paper, we show how the two best-studied sc enarios of belief change, belief revision and belief update, fit into our framework. (C) 1997 Elsevier Science B.V.