Abstract
In this paper, DeepPN is introduced. It is a modified version of PN-search. It introduces a procedure to solve the seesaw effect. DeepPN employs two important values associated with each node, viz. the usual proof number and a deep value. The deep value of a node is defined as the depth to which each child node has been searched. So, the deep value of a node shows the progress of the search in the depth direction. By mixing the proof numbers and the deep value, DeepPN works with two characteristics, viz., the best-first manner of search (equal to the original proof-number search) and the depth-first manner. By adjusting a parameter (called R in this paper) we can choose between best-first or depth-first behavior. In our experiments, we tried to find a balance between both manners of searching. As it turned out, best results were obtained at an R value in between the two extremes of best-first search (original proof number search) and depth-first search. Our experiments showed better results for DeepPN compared to the original PN-search: a point in between best-first and depth-first performed best. For random Othello and Hex positions, DeepPN works almost twice as good as PN-search. From the results, we may conclude that Deep Proof-Number Search outperforms PN-search considerably in Othello and Hex.
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This research is funded by a grant from the Japan Society for the Promotion of Science, in the framework of the Grant-in-Aid for Challenging Exploratory Research (grant number 26540189).
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Ishitobi, T., Plaat, A., Iida, H., van den Herik, J. (2015). Reducing the Seesaw Effect with Deep Proof-Number Search. In: Plaat, A., van den Herik, J., Kosters, W. (eds) Advances in Computer Games. ACG 2015. Lecture Notes in Computer Science(), vol 9525. Springer, Cham. https://doi.org/10.1007/978-3-319-27992-3_17
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