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Advances in Computer Games

ACG 2017: Advances in Computer Games pp 73–89Cite as

Deep df-pn and Its Efficient Implementations

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10664))

Abstract

Depth-first proof-number search (df-pn) is a powerful variant of proof-number search algorithms, widely used for AND/OR tree search or solving games. However, df-pn suffers from the seesaw effect, which strongly hampers the efficiency in some situations. This paper proposes a new proof number algorithm called Deep depth-first proof-number search (Deep df-pn) to reduce the seesaw effect in df-pn. The difference between Deep df-pn and df-pn lies in the proof number or disproof number of unsolved nodes. It is 1 in df-pn, while it is a function of depth with two parameters in Deep df-pn. By adjusting the value of the parameters, Deep df-pn changes its behavior from searching broadly to searching deeply. The paper shows that the adjustment is able to reduce the seesaw effect convincingly. For evaluating the performance of Deep df-pn in the domain of Connect6, we implemented a relevance-zone-oriented Deep df-pn that worked quite efficiently. Experimental results indicate that improvement by the same adjustment technique is also possible in other domains.

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Notes

  1. 1.

    http://www.jaist.ac.jp/is/labs/iida-lab/Deep_df_pn_Algorithm.pdf.

  2. 2.

    In this section, Deep df-pn is actually a relevance-zone-oriented Deep df-pn and the original df-pn is a relevance-zone-oriented df-pn for the application in Connect6.

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Acknowledgments

The authors thank the referees for their constructive comments and suggestions for improvements.

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Authors and Affiliations

  1. Graduate School of Information Science, Japan Advanced Institute of Science and Technology, Nomi, Japan

    Song Zhang & Hiroyuki Iida

  2. Leiden Centre of Data Science, Leiden, The Netherlands

    H. Jaap van den Herik

Authors
  1. Song Zhang
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  2. Hiroyuki Iida
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  3. H. Jaap van den Herik
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Corresponding author

Correspondence to Song Zhang .

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Editors and Affiliations

  1. Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, Limburg, The Netherlands

    Mark H.M. Winands

  2. Leiden Centre of Data Science, Leiden University, Leiden, Zuid-Holland, The Netherlands

    H. Jaap van den Herik

  3. Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Zuid-Holland, The Netherlands

    Walter A. Kosters

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Zhang, S., Iida, H., van den Herik, H.J. (2017). Deep df-pn and Its Efficient Implementations. In: Winands, M., van den Herik, H., Kosters, W. (eds) Advances in Computer Games. ACG 2017. Lecture Notes in Computer Science(), vol 10664. Springer, Cham. https://doi.org/10.1007/978-3-319-71649-7_7

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  • DOI: https://doi.org/10.1007/978-3-319-71649-7_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71648-0

  • Online ISBN: 978-3-319-71649-7

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