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Canadian Conference on Artificial Intelligence

Canadian AI 2016: Advances in Artificial Intelligence pp 270–275Cite as

Improving Conversation Engagement Through Data-Driven Agent Behavior Modification

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9673))

Abstract

E-learning systems based on a conversational agent (CA) provide the basis of an intuitive, engaging interface for the student. The goal of this paper is to propose an agent-based framework for providing an improved interaction between students and CA-based e-learning applications. Our framework models both the student and the CA and uses agents to represent data sources for each. We describe an implementation of the framework based on BDI (Belief-Desire-Intention) architecture and results of initial testing.

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References

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Author information

Authors and Affiliations

  1. School of Computing and Information Systems, Athabasca University, Athabasca, Canada

    Michael Procter & Fuhua Lin

  2. Faculty of Humanities and Social Sciences, Athabasca University, Athabasca, Canada

    Robert Heller

Authors
  1. Michael Procter
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  2. Fuhua Lin
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  3. Robert Heller
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Corresponding author

Correspondence to Michael Procter .

Editor information

Editors and Affiliations

  1. Lakehead University, Thunder Bay, Ontario, Canada

    Richard Khoury

  2. National Research Council Canada , Ottawa, Canada

    Christopher Drummond

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© 2016 Springer International Publishing Switzerland

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Procter, M., Lin, F., Heller, R. (2016). Improving Conversation Engagement Through Data-Driven Agent Behavior Modification. In: Khoury, R., Drummond, C. (eds) Advances in Artificial Intelligence. Canadian AI 2016. Lecture Notes in Computer Science(), vol 9673. Springer, Cham. https://doi.org/10.1007/978-3-319-34111-8_33

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  • DOI: https://doi.org/10.1007/978-3-319-34111-8_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-34110-1

  • Online ISBN: 978-3-319-34111-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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