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An Agent Architecture for Knowledge Discovery and Evolution

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KI 2021: Advances in Artificial Intelligence (KI 2021)

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Abstract

The abductive theory of method (ATOM) was recently proposed to describe the process that scientists use for knowledge discovery. In this paper we propose an agent architecture for knowledge discovery and evolution (KDE) based on ATOM. The agent incorporates a combination of ontologies, rules and Bayesian networks for representing different aspects of its internal knowledge. The agent uses an external AI service to detect unexpected situations from incoming observations. It then uses rules to analyse the current situation and a Bayesian network for finding plausible explanations for unexpected situations. The architecture is evaluated and analysed on a use case application for monitoring daily household electricity consumption patterns.

Supported by Hasso Plattner Institute (HPI) for Digital Engineering.

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Acknowledgements

This work was financially supported by the Hasso Plattner Institute for digital engineering through the HPI Research school at UCT.

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Authors and Affiliations

  1. Department of Computer Science, University of Cape Town (UCT), Cape Town, South Africa

    Tezira Wanyana & Deshendran Moodley

  2. Center for Artificial Intelligence Research (CAIR), Cape Town, South Africa

    Tezira Wanyana & Deshendran Moodley

Authors
  1. Tezira Wanyana
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  2. Deshendran Moodley
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Corresponding author

Correspondence to Tezira Wanyana .

Editor information

Editors and Affiliations

  1. Czech Technical University in Prague, Prague, Czech Republic

    Stefan Edelkamp

  2. University of Lübeck, Lübeck, Germany

    Ralf Möller

  3. University of Leoben, Leoben, Austria

    Elmar Rueckert

A Appendices

A Appendices

1.1 A.1 Appendix 1 -An Abstract Bayesian Network

Fig. 4.
figure 4

An abstract Bayesian network.

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1.2 A.2 Appendix 2 - Sample SWRL Rules

figure c

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Wanyana, T., Moodley, D. (2021). An Agent Architecture for Knowledge Discovery and Evolution. In: Edelkamp, S., Möller, R., Rueckert, E. (eds) KI 2021: Advances in Artificial Intelligence. KI 2021. Lecture Notes in Computer Science(), vol 12873. Springer, Cham. https://doi.org/10.1007/978-3-030-87626-5_18

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  • DOI: https://doi.org/10.1007/978-3-030-87626-5_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87625-8

  • Online ISBN: 978-3-030-87626-5

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