KR2023Proceedings of the 20th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 20th International Conference on Principles of Knowledge Representation and Reasoning

Rhodes, Greece. September 2-8, 2023.

Edited by

ISSN: 2334-1033
ISBN: 978-1-956792-02-7

Sponsored by
Published by

Copyright © 2023 International Joint Conferences on Artificial Intelligence Organization

ORLA: Learning Explainable Argumentation Models

  1. Cándido Otero(Information and Computing Sciences, Utrecht University)
  2. Dennis Craandijk(Information and Computing Sciences, Utrecht University, National Police Lab AI, Netherlands Police)
  3. Floris Bex(Information and Computing Sciences, Utrecht University, Institute for Law, Technology and Society, Tilburg University)


  1. Argumentation
  2. Symbolic reinforcement learning
  3. Explainable AI
  4. Applications that combine KR with machine learning


This paper presents ORLA (Online Reinforcement Learning Argumentation), a new approach for learning explainable symbolic argumentation models through direct exploration of the world. ORLA takes a set of expert arguments that promote some action in the world, and uses reinforcement learning to determine which of those arguments are the most effective for performing a task by maximizing a performance score. Thus, ORLA learns a preference ranking over the expert arguments such that the resulting value-based argumentation framework (VAF) can be used as a reasoning engine to select actions for performing the task. Although model-extraction methods exist that extract a VAF by mimicking the behavior of some non-symbolic model (e.g., a neural network), these extracted models are only approximations to their non-symbolic counterparts, which may result in both a performance loss and non-faithful explanations. Conversely, ORLA learns a VAF through direct interaction with the world (online learning), thus producing faithful explanations without sacrificing performance. This paper uses the Keepaway world as a case study and shows that models trained using ORLA not only perform better than those extracted from non-symbolic models but are also more robust. Moreover, ORLA is evaluated as a strategy discovery tool, finding a better solution than the expert strategy proposed by a related study.