Abstract
This paper investigates job-level (JL) algorithms to analyze opening positions for Connect6. The opening position analysis is intended for opening book construction, which is not covered by this paper. In the past, JL proof-number search (JL-PNS) was successfully used to solve Connect6 positions. Using JL-PNS, many opening plays that lead to losses can be eliminated from consideration during the opening game. However, it is unclear how the information of unsolved positions can be exploited for opening book construction. For this issue, this paper first proposes four heuristic metrics when using JL-PNS to estimate move quality. This paper then proposes a JL upper confidence tree (JL-UCT) algorithm and some heuristic metrics, one of which is the number of nodes in each candidate move’s subtree. In order to compare these metrics objectively, we proposed two kinds of measurement methods to analyze the suitability of these metrics when choosing best moves for a set of benchmark positions. The results show that for both metrics this node count heuristic metric for JL-UCT outperforms all the others, including the four for JL-PNS.
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Wei, TH. et al. (2014). Job-Level Algorithms for Connect6 Opening Position Analysis. In: Cazenave, T., Winands, M.H.M., Björnsson, Y. (eds) Computer Games. CGW 2014. Communications in Computer and Information Science, vol 504. Springer, Cham. https://doi.org/10.1007/978-3-319-14923-3_3
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DOI: https://doi.org/10.1007/978-3-319-14923-3_3
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