Research in Computing Science, Vol. 56, pp. 45-52, 2012.
Abstract: In this paper we show an application of possibilistic stable models to a learning situation. Our main result is that possibilistic stable models of possibilistic normal programs are also possibilistic safe beliefs of such programs. In any learning process, the learners arrive with their previous knowledge. In most cases, it is incomplete or it comes with some degree of uncertainty. Possibilistic Logic was developed as an approach to automated reasoning from uncertain or prioritized incomplete information. The standard possibilistic expressions are classical logic formulas associated with weights. Logic Programming is a very important tool in Artificial Intelligence. Safe beliefs were introduced to study properties and notions of answer sets and Logic Programming from a more general point of view. The stable model semantics is a declarative semantics for logic programs with default negation. In [1], the authors present possibilistic safe beliefs. In [2], the authors introduce possibilistic stable models.
Keywords: Normal logic programs, safe beliefs, possibilistic logic, possibilistic normal logic programs, possibilistic safe beliefs
PDF: Possibilistic Safe Beliefs vs. Possibilistic Stable Models
PDF: Possibilistic Safe Beliefs vs. Possibilistic Stable Models