On the relationship between circumscription and negation as failure
Artificial Intelligence
Logic programs with classical negation
Logic programming
Well-founded semantics coincides with three-valued stable semantics
Fundamenta Informaticae
Well founded semantics for logic programs with explicit negation
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
A knowledge representation framework based on autoepistemic logic of minimal beliefs
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Algorithmic Program DeBugging
Belief, Provability, and Logic Programs
JELIA '94 Proceedings of the European Workshop on Logics in Artificial Intelligence
Contradiction: When Avoidance Equals Removal - Part II
ELP '93 Proceedings of the 4th International Workshop on Extensions of Logic Programming
On Indefinite Databases and the Closed World Assumption
Proceedings of the 6th Conference on Automated Deduction
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In order to be able to explicitly reason about beliefs, we've introduced a non-monotonic formalism, called the Autoepistemic Logic of Beliefs, AEB, obtained by augmenting classical propositional logic with a belief operator, β. For this language we've defined the static autoepistemic expansions semantics. The resulting nonmonotonic knowledge representation framework turned out to be rather simple and yet quite powerful. Moreover, it has some very natural properties which sharply contrast with those of Moore's AEL. While static expansions seem to provide a natural and intuitive semantics for many belief theories, and, in particular, for all affirmative belief theories (which include the class of all normal and disjunctive logic programs), they often can lead to inconsistent expansions for theories in which (subjective) beliefs clash with the known (objective) information or with some other beliefs. In particular, this applies to belief theories (and to logic programs) with strong or explicit negation. In this paper we generalize AEB to avoid the acceptance of inconsistency provoking beliefs. We show how such AEB theories can be revised to prevent belief originated inconsistencies, and also to introduce declarative language level control over the revision level of beliefs, and apply it to the domains of diagnosis and declarative debugging. The generality of our AEB framework can capture and justify the methods that have been deployed to solve similar revision problems within the logic programming paradigm.