Guest Editorial: Reasoning With Inconsistent, Incomplete, and Uncertain Knowledge

The five papers in this special section focus on the management and analysis of uncertain, incomplete, and inconsistent information. This has become a crucial issue in the development of intelligent systems. Nowadays, such systems have to efficiently manage large amounts of information of different kinds, often represented in different formats and coming from different sources, such as databases, knowledge bases, sensor networks, as well as various data-driven applications. In the presence of such complex and heterogeneous forms of information, incompleteness, inconsistency, and/or inherent uncertainty inevitably arise. These scenarios call for innovative and intelligent approaches that, by leveraging AI techniques, can explicitly represent inconsistency, incompleteness, and uncertainty, and adequately deal with them. Such approaches are crucial to model realworld scenarios, making systems more effective and successful. Moreover, in many domains, knowledge is subject to frequent changes, so handling evolving knowledge is a key feature that knowledge-based systems should provide. In domains having high-impact consequences (e.g., healthcare and cybersecurity), intelligent systems should support human-in-the-loop models that provide tools to help users to understand and interpret the decisions they suggest, while tackling the challenges of inconsistency, incompleteness, and uncertainty. The AI community has also lately been facing the rising demand of explainable AI systems, which have inevitably to deal with inconsistency, incompleteness, and uncertainty.