<?xml version="1.0" encoding="UTF-8"?>
<reference anchor="I-D.francois-nmrg-ai-challenges" target="https://datatracker.ietf.org/doc/html/draft-francois-nmrg-ai-challenges-02">
   <front>
      <title>Research Challenges in Coupling Artificial Intelligence and Network Management</title>
      <author initials="J." surname="François" fullname="Jérôme François">
         <organization>University of Luxembourg and Inria</organization>
      </author>
      <author initials="A." surname="Clemm" fullname="Alexander Clemm">
         <organization>Futurewei Technologies, Inc.</organization>
      </author>
      <author initials="D." surname="Papadimitriou" fullname="Dimitri Papadimitriou">
         <organization>3NLab Belgium Reseach Center</organization>
      </author>
      <author initials="S." surname="Fernandes" fullname="Stenio Fernandes">
         <organization>Central Bank of Canada</organization>
      </author>
      <author initials="S." surname="Schneider" fullname="Stefan Schneider">
         <organization>Digital Railway (DSD) at Deutsche Bahn</organization>
      </author>
      <date month="March" day="13" year="2023" />
      <abstract>
	 <t>   This document is intended to introduce the challenges to overcome
   when network management problems may require to couple with AI
   solutions.  On the one hand, there are many difficult problems in
   Network Management that to this date have no good solutions, or where
   any solutions come with significant limitations and constraints.
   Artificial Intelligence may help produce novel solutions to those
   problems.  On the other hand, for several reasons (computational
   costs of AI solutions, privacy of data), distribution of AI tasks
   became primordial.  It is thus also expected that network SHOULD be
   operated efficiently to support those tasks.

   To identify the right set of challenges, the document defines a
   method based on the evolution and nature of NM problems.  This will
   be done in parallel with advances and the nature of existing
   solutions in AI in order to highlight where AI and NM have been
   already coupled together or could benefit from a higher integration.
   So, the method aims at evaluating the gap between NM problems and AI
   solutions.  Challenges are derived accordingly, assuming solving
   these challenges will help to reduce the gap between NM and AI.

	 </t>
      </abstract>
   </front>
   <seriesInfo name="Internet-Draft" value="draft-francois-nmrg-ai-challenges-02" />
   
</reference>
