KR2020Proceedings of the 17th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning

Rhodes, Greece. September 12-18, 2020.

Edited by

ISSN: 2334-1033
ISBN: 978-0-9992411-7-2

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Published by

Copyright © 2020 International Joint Conferences on Artificial Intelligence Organization

Towards an Inductive Logic Programming Approach for Explaining Black-Box Preference Learning Systems

  1. Fabio A. D'Asaro(University of Naples Federico II, CRdC Tecnologie)
  2. Matteo Spezialetti(University of Naples Federico II, University of L'Aquila)
  3. Luca Raggioli(University of Naples Federico II, CRdC Tecnologie)
  4. Silvia Rossi(University of Naples Federico II)


  1. Explainable AI-General
  2. Logic programming, answer set programming, constraint logic programming-General
  3. KR and machine learning, inductive logic programming, knowledge acquisition-General
  4. Applications of KR-General


In this paper we advocate the use of Inductive Logic Programming as a device for explaining black-box models, e.g. Support Vector Machines (SVMs), when they are used to learn user preferences. We present a case study where we use the ILP system ILASP to explain the output of SVM classifiers trained on preference datasets. Explanations are produced in terms of weak constraints, which can be easily understood by humans. We use ILASP both as a global and a local approximator for SVMs, score its fidelity, and discuss how its output can prove useful e.g. for interactive learning tasks and for identifying unwanted biases when the original dataset is not available. Finally, we highlight directions for further work and discuss relevant application areas.