<?xml version="1.0" encoding="UTF-8"?>
<reference anchor="I-D.irtf-nmrg-ai-challenges" target="https://datatracker.ietf.org/doc/html/draft-irtf-nmrg-ai-challenges-03">
   <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="4" year="2024" />
      <abstract>
	 <t>   This document is intended to introduce the challenges to overcome
   when Network Management (NM) problems may require to couple with
   Artificial Intelligence (AI) solutions.  On the one hand, there are
   many difficult problems in NM 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
   are 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-irtf-nmrg-ai-challenges-03" />
   
</reference>
