 Computer scientists have studied the aggregation of several independent and potentially conflicting sources of information into a consistent one. As mentioned in the introduction, examples are the combination of conflicting sensors’ information received by an agent, the aggregation of multiple databases to build an expert system, and multi-agent systems (Borgida and Imielinski 1984; Baral et al. 1992; Chawathe et al. 1994; Elmagarmid et al. 1999; Subrahmanian 1994; Kim 1995). Belief merging (or fusion) studies the aggregation of symbolic information (expressed in propositional logic) into a consistent base. As we shall see, the process of merging several bases has tight links with belief change (or belief revision), an active discipline since the 1980s across formal philosophy and computer science that models how human and artificial agents change their beliefs when they encounter new information. Revision, expansion and contraction are the three main types of theory change studied by Alchourrón, Gärdenfors and Makinson (the so-called AGM theory) who also provided rationality postulates for each of them (Alchourrón et al. 1985; Gärdenfors 1988). In belief revision, the focus is on how one belief base changes in face of new, completely reliable information and this new piece may be conflicting with existing beliefs in the base. When I learn that a new acquaintance Rob has a child, I simply add the piece of information and eventually derive new consequences (this is expansion). The most interesting case, though, is when the new information conflicts with previously held beliefs. Suppose that from a common friend I understood that Rob had no kids. Now I learn from Rob himself that he has a child. In order to accommodate the new information, I need to perform a revision, which consists of removing the wrong belief that he had no kids (and all the other beliefs that may depend on that), add the new input that in fact he has a child, and derive possibly new consequences (see Hansson 2011 for an overview and Fermé and Hansson 2018 for a comprehensive introduction to belief change). As for belief revision, in merging the term “knowledge” is used in a broader sense than in the epistemological literature, such that “knowledge” refers to formulas accepted by an agent (i.e., formulas in her knowledge base), which are not necessarily true. Then, “knowledge base” and “belief base” are used interchangeably. For Grégoire and Konieczny (2006) belief merging operators can be used to aggregate also other types of information than knowledge and beliefs, such as goals, observations, and norms.