Sequencing of autonomous network functions using explainable AI methods

Premnath K. Narayanan, David K. Harrison, Zahra Salimi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Autonomous network functions (ANFs) are activated to achieve a specific objective. (e.g.: Load balancing, coverage and capacity optimization, energy saving across the network). Many times, activating the ANFs does not meet the specific objective predominantly due to external factors [1]. This paper introduces how explainable AI (xAI) methods such as feature impact analysis, dependency plot and other interpretable machine learning algorithms can be used for identifying such external factors and in turn sequencing the ANFs for meeting the objective. The paper concludes by introducing counterfactual and recourse algorithms as further research possibilities that goes beyond xAI for getting favorable outcome from ANFs.
Original languageEnglish
Title of host publication2022 3rd International Conference on Intelligent Engineering and Management (ICIEM)
PublisherIEEE
Pages973-977
Number of pages5
ISBN (Electronic)9781665467568
ISBN (Print)9781665467575
DOIs
Publication statusPublished - 17 Aug 2022

Publication series

Name2022 3rd International Conference on Intelligent Engineering and Management (ICIEM)
PublisherIEEE

Keywords

  • sequential analysis
  • machine learning algorithims
  • load management
  • communications technology
  • object recognition
  • artificial intelligence
  • optimization
  • explainable AI
  • autonomous network functions

ASJC Scopus subject areas

  • Information Systems and Management
  • Artificial Intelligence
  • Engineering (miscellaneous)
  • Health Informatics
  • Management of Technology and Innovation
  • Computer Science Applications

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