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KI - Künstliche Intelligenz

, Volume 33, Issue 1, pp 101–104 | Cite as

Multi-Context Reasoning in Continuous Data-Flow Environments

  • Stefan EllmauthalerEmail author
Dissertation and Habilitation Abstracts
  • 23 Downloads

Abstract

The field of artificial intelligence, especially research on knowledge representation and reasoning, has originated a large variety of formats, languages, and formalisms. Over the decades many different tools emerged to use these underlying concepts. Each one has been designed with some specific application in mind. In the century of Industry 4.0 and the Internet of Things, a formal way to uniformly exchange information, such as knowledge and belief, is imperative. That alone is not enough, because even more systems get integrated into this online setting and nowadays we are confronted with a huge amount of continuously flowing data. Therefore a solution is needed to both, allowing the integration of information and dynamic reaction to the data. My thesis aims to present a unique and novel pair of formalisms to tackle these two important needs by proposing an abstract and general solution.

Keywords

Multi-context systems Stream reasoning Knowledge representation Nonmonotonic reasoning 

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Copyright information

© Gesellschaft für Informatik e.V. and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Intelligent Systems Group, Computer Science InstituteLeipzig UniversityLeipzigGermany

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