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Data Mining Techniques for Proactive Fault Diagnostics of Electronic Gaming Machines

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Advances in Artificial Intelligence (Canadian AI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6085))

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Abstract

This paper details the preliminary research into modeling the behavior of Electronic Gaming Machines (EGM) for the task of proactive fault diagnostics. The EGMs operate within a state space and therefore their behavior was modeled, using supervised learning, as the frequency at which a given machine is operating in a particular state. The results indicated that EGMs did exhibit measurably different behavior when they were about to experience a fault and these relationships were modeled effectively by several algorithms.

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References

  1. Malkoff, D.: A framework for real-time fault detection and diagnosis using temporal data. International Journal for Artificial Intelligence in Engineering 2(2), 97–111 (1987)

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  2. Yairi, T., Kato, Y., Hori, K.: Fault detection by mining association rules from house-keeping data. In: Proc. of International Symposium on Artificial Intelligence, Robotics and Automation in Space, Citeseer (2001)

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  3. Sylvain, L., Fazel, F., Stan, M.: Data mining to predict aircraft component replacement. IEEE Intell. Syst. 14(6), 59–65 (1999)

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Author information

Authors and Affiliations

  1. Faculty of Computer Science, Artificial Intelligence Group, University of York, UK

    Matthew Butler

  2. Faculty of Computer Science, Dalhousie University, Canada

    Matthew Butler & Vlado Kešelj

Authors
  1. Matthew Butler
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  2. Vlado Kešelj
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Editor information

Editors and Affiliations

  1. NLP Technologies Inc., 1255 University Street, H3B 3W9, Montreal, Quebec, Canada

    Atefeh Farzindar

  2. Dalhousie University, Faculty of Computer Science, 6050 University Ave, Halifax, B3H 1W5, Nova Scotia, Canada

    Vlado Kešelj

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© 2010 Springer-Verlag Berlin Heidelberg

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Butler, M., Kešelj, V. (2010). Data Mining Techniques for Proactive Fault Diagnostics of Electronic Gaming Machines. In: Farzindar, A., Kešelj, V. (eds) Advances in Artificial Intelligence. Canadian AI 2010. Lecture Notes in Computer Science(), vol 6085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13059-5_48

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  • DOI: https://doi.org/10.1007/978-3-642-13059-5_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13058-8

  • Online ISBN: 978-3-642-13059-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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