Identifying Soft Cores in Propositional Formulae
Résumé
In view of the emergence of explainable AI, many new concepts intend to explain why systems exhibit certain behaviors while other behaviors are excluded. When dealing with constraints, explanations can take the form of subsets having few solutions, while being sufficiently small for ensuring that they are intelligible enough. To make it formal, we present a new notion, called soft core, characterizing both small and highly constrained parts of GCNF instances, whether satisfiable or not. Soft cores can be used in unsatisfiable instances as an alternative to MUSes (Minimal Unsatisfiable Subformulae) or in satisfiable ones as an alternative to MESes (Minimal Equivalent Subformulae). We also provide an encoding to translate soft cores instances into MAX#SAT instances. Finally, we propose a new method to solve MAX#SAT instances and we use it to extract soft cores.
Domaines
Intelligence artificielle [cs.AI]
Origine : Fichiers produits par l'(les) auteur(s)