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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/15854

Title: Deep df-pn and Its Efficient Implementations
Authors: Song, Zhang
Iida, Hiroyuki
Herik, Jaap van den
Keywords: df-pn
seesaw effect
Issue Date: 2017-12-22
Publisher: Springer
Magazine name: Lecture Notes in Computer Science
Volume: 10664
Start page: 73
End page: 89
DOI: 10.1007/978-3-319-71649-7_7
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. The experimental results indicate that improving efficiency by the same adjustment technique is also possible in other domains.
Rights: This is the author-created version of Springer, Song Zhang, Hiroyuki Iida, H. Jaap van den Herik, Lecture Notes in Computer Science, 10664, 2017, 73-89. The original publication is available at www.springerlink.com, http://dx.doi.org/10.1007/978-3-319-71649-7_7
URI: http://hdl.handle.net/10119/15854
Material Type: author
Appears in Collections:b10-1. 雑誌掲載論文 (Journal Articles)

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