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b10-1. 雑誌掲載論文 >
このアイテムの引用には次の識別子を使用してください:
http://hdl.handle.net/10119/15854
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| タイトル: | Deep df-pn and Its Efficient Implementations |
| 著者: | Song, Zhang
Iida, Hiroyuki
Herik, Jaap van den |
| キーワード: | df-pn
seesaw effect
parameters
Connect6 |
| 発行日: | 2017-12-22 |
| 出版者: | Springer |
| 誌名: | Lecture Notes in Computer Science |
| 巻: | 10664 |
| 開始ページ: | 73 |
| 終了ページ: | 89 |
| DOI: | 10.1007/978-3-319-71649-7_7 |
| 抄録: | 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 |
| 資料タイプ: | author |
| 出現コレクション: | b10-1. 雑誌掲載論文 (Journal Articles)
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このアイテムのファイル:
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記述 |
サイズ | 形式 |
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| 23404.pdf | | 958Kb | Adobe PDF | 見る/開く |
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