Practical handling of exception-tainted rules and independence information in possibilistic logic

This paper provides a survey of possibilistic logic as a simple and efficient tool for handling nonmonotonic reasoning, with some emphasis on algorithmic issues. In our previous works, two well-known nonmonotonic systems have been encoded in the possibility theory framework: the preferential inference based on System P, and the rational closure inference proposed by Lehmann and Magidor which relies on System P augmented with a rational monotony postulate. System P is known to provide reasonable but very cautious conclusions, and in particular, preferential inference is blocked by the presence of "irrelevant" properties. When using Lehmann's rational closure, the inference machinery, which is then more productive, may still remain too cautious, or on the contrary, provide counter-intuitive conclusions. The paper proposes an approach to overcome the cautiousness of System P and the problems encountered by the rational closure inference. This approach takes advantage of (contextual) independence assumptions of the form: the fact that gamma is true (or is false) does not affect the validity of the rule "normally if alpha then beta". The modelling of such independence assumptions is discussed in the possibilistic framework. Moreover, we show that when a counter-intuitive conclusion of a set of defaults can be inferred, it is always possible to repair the set of defaults by adding suitable information so as to produce the desired conclusions and block unsuitable ones.