How to Choose Weightings to Avoid Collisions in a Restricted Penalty Logic

Nathalie Chetcuti-Sperandio (1) , Sylvain Lagrue (1)

1 CRIL - Centre de Recherche en Informatique de Lens

Penalty Logic is a natural and commonsense Knowl- edge Representation technique to deal with potentially inconsistent beliefs. Penalty Logic allows some kind of compensation between different pieces of information. But one of the main and less studied flaws of Penalty Logic is the influence of the choice of weights on in- ference: the same pieces of information can provide ex- tremely different results just by changing some weights. This paper concentrates on weightings and on the prob- lem of collisions between interpretations which yield weak conclusions. It focuses more particularly on a family of weightings, the σ -weightings. We show that some of these weightings avoid collisions but that in the meanwhile they disable the mechanism of compensa- tion (and so the interest) of Penalty Logic. We establish then that two of them are suitable for avoiding collisions and maintaining compensation. We obtain their logical characterizations while considering the weightings only and not the associated formulas. Finally, we propose an original weighting, the Paralex Weighting, that improves even more the previous weightings.

Domaines: Intelligence artificielle [cs.AI]

Nathalie Chetcuti-Sperandio, Sylvain Lagrue. How to Choose Weightings to Avoid Collisions in a Restricted Penalty Logic. 11th International Conference on Principles of Knowledge Representation and Reasoning (KR'08), 2008, Sydney, Australia. pp.340-347. ⟨hal-00866556⟩