Lexicographic Inference for Partially Ordered Belief Bases

Safa Yahi (1) , Salem Benferhat (1) , Sylvain Lagrue (1) , Mariette Serayet (2) , Odile Papini (2)

1 CRIL - Centre de Recherche en Informatique de Lens
2 LSIS - Laboratoire des Sciences de l'Information et des Systèmes

Coherence-based approaches are quite popular to reason under inconsistency. Most of them are defined with respect to totally preordered belief bases such as the lexicographic inference which is known to have desirable properties from theoretical, practical and psychological points of view. However, partially preordered belief bases offer much more flexibility to represent efficiently incomplete knowledge and to avoid comparing unrelated pieces of information. In this paper, we propose a lexicographic inference for partially preordered belief bases that extends the classical one. On one hand, we define a natural inference relation which con- sists in applying classical lexicographic inference from all compatible totally preordered belief bases. On the other hand, we propose a novel cardinality-based preorder between consistent subbases. This cardinality- based preorder can be indifferently applied on partially or totally preordered belief bases. Then, applying classical inference on the preferred consistent subbases, according to this preorder, provides another lexicographic inference relation for partially preordered belief bases. Interestingly enough, we show that the second inference is covered by the first one. Lastly, a semantic characterization of these two definitions is provided.

Domaines: Intelligence artificielle [cs.AI]

Safa Yahi, Salem Benferhat, Sylvain Lagrue, Mariette Serayet, Odile Papini. Lexicographic Inference for Partially Ordered Belief Bases. 11th International Conference on Principles of Knowledge Representation and Reasoning (KR'08), 2008, Sydney, Australia. pp.507-516. ⟨hal-00867165⟩