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http://hdl.handle.net/10119/15854
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| Title: | Deep df-pn and Its Efficient Implementations |
| Authors: | Song, Zhang
Iida, Hiroyuki
Herik, Jaap van den |
| Keywords: | df-pn
seesaw effect
parameters
Connect6 |
| 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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