The Role of Non-monotonic Reasoning in Future Development of Artificial Intelligence (Dagstuhl Perspectives Workshop 19072)

Authors Anthony Hunter, Gabriele Kern-Isberner, Thomas Meyer, Renata Wassermann and all authors of the abstracts in this report



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Anthony Hunter
Gabriele Kern-Isberner
Thomas Meyer
Renata Wassermann
and all authors of the abstracts in this report

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Anthony Hunter, Gabriele Kern-Isberner, Thomas Meyer, and Renata Wassermann. The Role of Non-monotonic Reasoning in Future Development of Artificial Intelligence (Dagstuhl Perspectives Workshop 19072). In Dagstuhl Reports, Volume 9, Issue 2, pp. 73-90, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019) https://doi.org/10.4230/DagRep.9.2.73

Abstract

This report documents the program and the outcomes of Dagstuhl Perspectives Workshop 19072 "The Role of Non-monotonic Reasoning in Future Development of Artificial Intelligence". The workshop brought together researchers both from core topics and peripheral areas of non-monotonic reasoning (NMR), but also attracted researchers from other scientific domains in which recent developments have shown an increased relevance of NMR topics. The overall goal of this workshop was to reshape NMR as a core methodology for artificial intelligence being able to meet present and future challenges. Participants of this workshop discussed in what shape NMR would be useful for future AI, and how NMR can be developed for those requirements. The workshop started with brief survey talks and had some technical talks on central topics of NMR afterwards. These were followed by working groups on core aspects of NMR and potential links with learning. On the last day of the seminar, each working group presented their ideas and future plans. The workshop closed with a plenary discussion on the future of NMR.

Subject Classification

Keywords
  • Artificial intelligence
  • Knowledge representation and reasoning
  • Nonmonotonic
  • default reasoning and belief revision
  • Probabilistic reasoning
  • Logic programming and answer set programming
  • Ontology engineering
  • Cognitive science
  • Machine learning

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