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