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Possibilistic networks and possibilistic logic bases are important tools to deal with uncertain pieces of information. Both of them offer a compact representation of possibility distributions. This paper studies a new representation format, called hybrid possibilis-tic networks, which cover both standard possibilis-tic networks and possibilistic knowledge… (More)

Possibilistic networks are important tools for dealing with uncertain pieces of information. For multiply-connected networks, it is well known that the inference process is a hard problem. This paper studies a new representation of possibilistic networks, called hybrid pos-sibilistic networks. The uncertainty is no longer represented by local conditional… (More)

This paper contains two important contributions for the development of possibilistic causal networks. The first one concerns the representation of interventions in pos-sibilistic networks. We provide the counterpart of the " DO " operator, recently introduced by Pearl, in possibility theory framework. We then show that interventions can equivalently be… (More)

Ascribing causality amounts to determining what elements in a sequence of reported facts can be related in a causal way, on the basis of some knowledge about the course of the world. The paper offers a comparison of a large span of formal models (based on structural equations, non-monotonic consequence relations, trajectory preference relations,… (More)

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