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Learning Condition–Action Rules for Personalised Journey Recommendations

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Rules and Reasoning (RuleML+RR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11092))

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Abstract

We apply a learning classifier system, XCSI, to the task of providing personalised suggestions for passenger onward journeys. Learning classifier systems combine evolutionary computation with rule-based machine learning, altering a population of rules to achieve a goal through interaction with the environment. Here XCSI interacts with a simulated environment of passengers travelling around the London Underground network, subject to disruption. We show that XCSI successfully learns individual passenger preferences and can be used to suggest personalised adjustments to the onward journey in the event of disruption.

This research was partly funded by the Department for Transport, via Innovate UK and the Accelerating Innovation in Rail (AIR) Round 4 programme, under the Onward Journey Planning Assistant (OJPA) project.

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Notes

  1. 1.

    Note that in the simple example here we depict a match as immediately triggering the rule in question – the actual action selection is more complex.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Surrey, Guildford, GU2 7XH, UK

    Matthew R. Karlsen & Sotiris Moschoyiannis

Authors
  1. Matthew R. Karlsen
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  2. Sotiris Moschoyiannis
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Correspondence to Sotiris Moschoyiannis .

Editor information

Editors and Affiliations

  1. Computer Science, Freie Universität Berlin, Berlin, Germany

    Christoph Benzmüller

  2. University of Calabria, Rende, Italy

    Francesco Ricca

  3. Université du Luxembourg, Esch-sur-Alzette, Luxembourg

    Xavier Parent

  4. SINTEF/University of Oslo, Oslo, Norway

    Dumitru Roman

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Karlsen, M.R., Moschoyiannis, S. (2018). Learning Condition–Action Rules for Personalised Journey Recommendations. In: Benzmüller, C., Ricca, F., Parent, X., Roman, D. (eds) Rules and Reasoning. RuleML+RR 2018. Lecture Notes in Computer Science(), vol 11092. Springer, Cham. https://doi.org/10.1007/978-3-319-99906-7_21

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  • DOI: https://doi.org/10.1007/978-3-319-99906-7_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99905-0

  • Online ISBN: 978-3-319-99906-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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