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International Conference on Case-Based Reasoning

ICCBR 2021: Case-Based Reasoning Research and Development pp 171–185Cite as

Inferring Case-Based Reasoners’ Knowledge to Enhance Interactivity

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12877))

Abstract

When interacting with a human user, an artificial intelligence needs to have a clear model of the human’s behaviour to make the correct decisions, be it recommending items, helping the user in a task or teaching a language. In this paper, we explore the feasibility of modelling the human as a case-based reasoning agent through the question of how to infer the state of a CBR agent from interaction data. We identify the main parameters to be inferred, and propose a Bayesian belief update as a possible way to infer both the parameters of the agent and the content of their case base. We illustrate our ideas with the simple application of an agent learning grammar rules throughout a sequence of observations.

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Notes

  1. 1.

    www.kotus.fi. The link to the list of Finnish words: kaino.kotus.fi/sanat/nykysuomi.

  2. 2.

    www.wiktionary.org.

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Author information

Authors and Affiliations

  1. Helsinki Institute for Information Technology HIIT, Helsinki, Finland

    Pierre-Alexandre Murena & Marie Al-Ghossein

  2. Department of Computer Science, Aalto University, Espoo, Finland

    Pierre-Alexandre Murena

  3. Department of Computer Science, University of Helsinki, Helsinki, Finland

    Marie Al-Ghossein

Authors
  1. Pierre-Alexandre Murena
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  2. Marie Al-Ghossein
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Corresponding author

Correspondence to Pierre-Alexandre Murena .

Editor information

Editors and Affiliations

  1. Universidad Complutense de Madrid, Madrid, Spain

    Antonio A. Sánchez-Ruiz

  2. Knexus Research Corp., National Harbor, MD, USA

    Michael W. Floyd

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Murena, PA., Al-Ghossein, M. (2021). Inferring Case-Based Reasoners’ Knowledge to Enhance Interactivity. In: Sánchez-Ruiz, A.A., Floyd, M.W. (eds) Case-Based Reasoning Research and Development. ICCBR 2021. Lecture Notes in Computer Science(), vol 12877. Springer, Cham. https://doi.org/10.1007/978-3-030-86957-1_12

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  • DOI: https://doi.org/10.1007/978-3-030-86957-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86956-4

  • Online ISBN: 978-3-030-86957-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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