Handbook of logic in artificial intelligence and logic programming (vol. 3)
Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
Applications of intuitionistic logic in Answer Set Programming
Theory and Practice of Logic Programming
Possibilistic uncertainty handling for answer set programming
Annals of Mathematics and Artificial Intelligence
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We define what we call 'possibilistic intermediate logic (PIL)'; we present results analogous to those of the well-known intermediate logic, such as a deduction theorem, a generalised version of the deduction theorem, a cut rule, a weak version of a refutation theorem, a substitution theorem and Glivenko's theorem. Also, we present a definition for 'possibilistic safe beliefs'. This definition allows us to establish a relation between safe beliefs, as presented on 'applications of intuitionistic logic in answer set programming' by Osorio et al., and our version for possibilistic intermediate logic (PILX). We also present a characterisation of possibilistic safe beliefs for possibilistic normal logic programs.