Special issue on challenges for reasoning under uncertainty, inconsistency, vagueness, and preferences: a topical snapshot

Journal article

Managing uncertainty, inconsistency, vagueness, and preferences has been extensively explored in artificial intelligence (AI). During the recent years, especially with the emerging of smart services and devices, technologies for managing uncertainty, inconsistency, vagueness, and preferences to tackle the problems of dynamic, real-world scenarios have started to play a key role also in other areas, such as information systems and the (Social and/or Semantic) Web. These application areas have sparked another wave of strong interest into formalisms and logics for dealing with uncertainty, inconsistency, vagueness, and preferences. Important examples are fuzzy and probabilistic approaches for description logics, or rule systems for handling vagueness and uncertainty in the Semantic Web, or formalisms for handling user preferences in the context of ontological knowledge in the Social Semantic Web. While scalability of these approaches is an important issue to be addressed, also the need for combining various of these approaches with each other and/or with more classical ways of reasoning have become obvious (hybrid reasoning under uncertainty). This special issue presents several state-of-the-art formalisms and methodologies for managing uncertainty, inconsistency, vagueness, and preferences.

Authors: Kern-Isberner, G; Lukasiewicz, T

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer Verlag
• Journal: KI - Künstliche Intelligenz
• Volume: 31
• Issue: 1
• Pages: 5–8
• Publication date: 2017-03-15
• Acceptance date: 2016-12-19
• DOI: 10.1007/s13218-016-0479-z
• EISSN: 1610-1987
• ISSN: 0933-1875