Online Incremental Learning with Abstract Argumentation Frameworks

H. Ayoobi*, M. Cao, R. Verbrugge, B. Verheij

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


The environment around general-purpose service robots has a dynamic nature. Accordingly, even the robot's programmer cannot predict all the possible external failures which the robot may confront. This research proposes an online incremental learning method that can be further used to autonomously handle external failures originating from a change in the environment. Existing research typically offers special-purpose solutions. Furthermore, the current incremental online learning algorithms can not generalize well with just a few observations. In contrast, our method extracts a set of hypotheses, which can then be used for finding the best recovery behavior at each failure state. The proposed argumentation-based online incremental learning approach uses an abstract and bipolar argumentation framework to extract the most relevant hypotheses and model the defeasibility relation between them. This leads to a novel online incremental learning approach that overcomes the addressed problems and can be used in different domains including robotic applications. We have compared our proposed approach with state-of-the-art online incremental learning approaches and an approximation-based reinforcement learning method. The experimental results show that our approach learns more quickly with a lower number of observations and also has higher final precision than the other methods.

Original languageEnglish
Title of host publication1st Workshop on Argumentation and Machine Learning, ArgML 2022
Number of pages16
Publication statusPublished - 2022
Event1st Workshop on Argumentation and Machine Learning, ArgML 2022 - Cardiff, United Kingdom
Duration: 13-Sep-202213-Sep-2022

Publication series

NameCEUR Workshop Proceedings
ISSN (Print)1613-0073


Conference1st Workshop on Argumentation and Machine Learning, ArgML 2022
Country/TerritoryUnited Kingdom


  • Argumentation Theory
  • Argumentation-Based Learning
  • General Purpose Service Robots
  • Online Incremental Learning

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