Main Issues in Belief Revision, Belief Merging and Information Fusion

This chapter focuses on the dynamics of information represented in logical or numerical formats, from pioneering works to recent developments. The logical approach to belief change is a topic that has been extensively studied in Artificial Intelligence, starting in the mid-seventies. In this problem, logical formulas represent beliefs held by an intelligent agent that must be revised upon receiving new information that conflicts with prior beliefs and usually has priority over them. In contrast, in the merging problem, the logical theories that must be combined have equal priority. Such logical approaches recalled here make sense for merging beliefs as well as goals, even if each of these problems cannot be reduced to the other. In the last part, we discuss a number of issues pertaining to the fusion and the revision of uncertainty functions representing epistemic states, such as probability measures, possibility measures and belief functions. The need to cope with logical inconsistency plays a major role in these problems. The ambition of this chapter is not to provide an exhaustive bibliography, but rather to propose an overview of basic notions, main results and new research issues in this area.