Skip to main content
SpringerLink
Log in
Book cover

International Workshop on New Frontiers in Mining Complex Patterns

NFMCP 2016: New Frontiers in Mining Complex Patterns pp 248–262Cite as

Mining Chess Playing as a Complex Process

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10312))

Abstract

The main objective of this paper is checking whether, and to what extent, advanced process mining techniques can support efficient and effective knowledge discovery in complex domains. This is done on chess playing, cast as a process. A secondary objective is checking whether the discovered information can provide interesting insight in the game rules and strategies, and/or may support effective game playing in future matches. Experimental results provide a positive answer to the former question, and encouraging clues on the latter.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Agrawal, R., Gunopulos, D., Leymann, F.: Mining process models from workflow logs. In: Schek, H.-J., Alonso, G., Saltor, F., Ramos, I. (eds.) EDBT 1998. LNCS, vol. 1377, pp. 467–483. Springer, Heidelberg (1998). doi:10.1007/BFb0101003

    Google Scholar 

  2. Cook, J.E., Wolf, A.L.: Discovering models of software processes from event-based data. Technical report CU-CS-819-96, Department of Computer Science, University of Colorado (1996)

    Google Scholar 

  3. Cook, J.E., Wolf, A.L.: Event-based detection of concurrency. Technical report CU-CS-860-98, Department of Computer Science, University of Colorado (1998)

    Google Scholar 

  4. de Medeiros, A.K.A., van Dongen, B.F., van der Aalst, W.M.P., Weijters, A.J.M.M.: Process mining: extending the \(\alpha \)-algorithm to mine short loops. In: WP 113, BETA Working Paper Series. Eindhoven University of Technology (2004)

    Google Scholar 

  5. Ferilli, S.: Woman: logic-based workflow learning and management. IEEE Trans. Syst. Man Cybern. Syst. 44, 744–756 (2014)

    Article  Google Scholar 

  6. Ferilli, S., Esposito, F.: A logic framework for incremental learning of process models. Fundam. Inform. 128, 413–443 (2013)

    MathSciNet  MATH  Google Scholar 

  7. Herbst, J., Karagiannis, D.: An inductive approach to the acquisition and adaptation of workflow models. In: Proceedings of the IJCAI 1999 Workshop on Intelligent Workflow and Process Management: The New Frontier for AI in Business, pp. 52–57 (1999)

    Google Scholar 

  8. Lai, M.: Giraffe: using deep reinforcement learning to play chess (2015). CoRR, abs/1509.01549

    Google Scholar 

  9. Lloyd, J.W.: Foundations of Logic Programming, 2nd edn. Springer, Heidelberg (1987)

    Book  MATH  Google Scholar 

  10. Oshri, B., Khandwala, N.: Predicting moves in chess using convolutional neural networks. In: Stanford University Course Project Reports - CS231n: Convolutional Neural Networks for Visual Recognition, Winter 2016

    Google Scholar 

  11. Pesic, M., van der Aalst, W.M.P.: A declarative approach for flexible business processes management. In: Eder, J., Dustdar, S. (eds.) BPM 2006. LNCS, vol. 4103, pp. 169–180. Springer, Heidelberg (2006). doi:10.1007/11837862_18

    Chapter  Google Scholar 

  12. van der Aalst, W.M.P.: The application of petri nets to workflow management. J. Circuits Syst. Comput. 8, 21–66 (1998)

    Article  Google Scholar 

  13. van der Aalst, W.M.P., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16, 1128–1142 (2004)

    Article  Google Scholar 

  14. Weijters, A.J.M.M., van der Aalst, W.M.P.: Rediscovering workflow models from event-based data. In: Hoste, V., De Pauw, G. (eds.) Proceedings of the 11th Dutch-Belgian Conference of Machine Learning (Benelearn 2001), pp. 93–100 (2001)

    Google Scholar 

  15. Wen, L., Wang, J., Sun, J.: Detecting implicit dependencies between tasks from event logs. In: Zhou, X., Li, J., Shen, H.T., Kitsuregawa, M., Zhang, Y. (eds.) APWeb 2006. LNCS, vol. 3841, pp. 591–603. Springer, Heidelberg (2006). doi:10.1007/11610113_52

    Chapter  Google Scholar 

Download references

Acknowledgments

This work was partially funded by the Italian PON 2007–2013 project PON02_00563_3489339 ‘Puglia@Service’.

Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Università di Bari, Bari, Italy

    Stefano Ferilli & Sergio Angelastro

  2. Centro Interdipartimentale di Logica e Applicazioni, Università di Bari, Bari, Italy

    Stefano Ferilli

Authors
  1. Stefano Ferilli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sergio Angelastro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stefano Ferilli .

Editor information

Editors and Affiliations

  1. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  2. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  3. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Corrado Loglisci

  4. ICAR-CNR, Rende, Italy

    Elio Masciari

  5. University of North Carolina, Charlotte, North Carolina, USA

    Zbigniew W. Raś

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Ferilli, S., Angelastro, S. (2017). Mining Chess Playing as a Complex Process. In: Appice, A., Ceci, M., Loglisci, C., Masciari, E., Raś, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2016. Lecture Notes in Computer Science(), vol 10312. Springer, Cham. https://doi.org/10.1007/978-3-319-61461-8_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-61461-8_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61460-1

  • Online ISBN: 978-3-319-61461-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation