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Multi-Context Reasoning in Continuous Data-Flow Environments

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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.

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Notes

  1. Note that it is not intended to replace a medical doctor or care staff, it is intended to filter non-harmful situations from harmful ones to support the care worker.

  2. If the hardest context is in the polynomial hierarchy the overall complexity rises by one level on the hierarchy.

  3. That language is a set of abstract information and its content is limited by context formalisms and evaluation methods.

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Authors and Affiliations

  1. Intelligent Systems Group, Computer Science Institute, Leipzig University, Leipzig, Germany

    Stefan Ellmauthaler

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  1. Stefan Ellmauthaler
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Correspondence to Stefan Ellmauthaler.

Additional information

Parts of this work have been funded by DFG Project BR-1817/7-2 and the DFG Research Unit FOR 1513.

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Ellmauthaler, S. Multi-Context Reasoning in Continuous Data-Flow Environments. Künstl Intell 33, 101–104 (2019). https://doi.org/10.1007/s13218-018-00570-1

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