Argumentative inference in uncertain and inconsistent knowledge bases
This work addresses the challenge of making reliable inferences in AI systems with uncertain or conflicting data, which is incremental as it builds on existing methods for handling inconsistency.
The paper tackles the problem of reasoning from inconsistent knowledge bases by investigating argumentative-consequence relations that consider consistent arguments for and against conclusions, and proposes a paraconsistent-like treatment for prioritized knowledge bases using possibility theory.
This paper presents and discusses several methods for reasoning from inconsistent knowledge bases. A so-called argumentative-consequence relation taking into account the existence of consistent arguments in favor of a conclusion and the absence of consistent arguments in favor of its contrary, is particularly investigated. Flat knowledge bases, i.e. without any priority between their elements, as well as prioritized ones where some elements are considered as more strongly entrenched than others are studied under different consequence relations. Lastly a paraconsistent-like treatment of prioritized knowledge bases is proposed, where both the level of entrenchment and the level of paraconsistency attached to a formula are propagated. The priority levels are handled in the framework of possibility theory.