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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Buro, M.: Toward Opening Book Learning. ICCA Journal 22(2), 98–102 (1999)
Hyatt, R.M.: Book Learning-a Methodology to Tune an Opening Book Automatically. ICCA Journal 22(1), 3–12 (1999)
Lincke, T.R.: Strategies for the Automatic Construction of Opening Books. Computers and Games, 74–86 (2001)
Audouard, P., Chaslot, G., Hoock, J.-B., Perez, J., Rimmel, A., Teytaud, O.: Grid Coevolution for Adaptive Simulations: Application to the Building of Opening Books in the Game of Go. In: Giacobini, M., et al. (eds.) EvoWorkshops 2009. LNCS, vol. 5484, pp. 323–332. Springer, Heidelberg (2009)
Chaslot, G.M.J.-B., Hoock, J.-B., Perez, J., Rimmel, A., Teytaud, O., Winands, M.H.M.: Meta Monte-Carlo Tree Search for Automatic Opening Book Generation. In: Proceedings of the IJCAI 2009 Workshop on General Intelligence in Game Playing Agents, Pasadena, California, pp. 7–12 (2009)
Gaudel, R., Hoock, J.-B., Pérez, J., Sokolovska, N., Teytaud, O.: A Principled Method for Exploiting Opening Books. In: van den Herik, H.J., Iida, H., Plaat, A. (eds.) CG 2010. LNCS, vol. 6515, pp. 136–144. Springer, Heidelberg (2011)
Wu, I., Huang, D., Chang, H.: Connect6. ICGA Journal 28(4), 234–242 (2005)
Knuth, D.E., Moore, R.W.: An Analysis of Alpha-Beta Pruning. Artificial Intelligence 6(4), 293–326 (1975)
Karapetyan, A., Lorentz, R.J.: Generating an opening book for amazons. In: van den Herik, H.J., Björnsson, Y., Netanyahu, N.S. (eds.) CG 2004. LNCS, vol. 3846, pp. 161–174. Springer, Heidelberg (2006)
Kloetzer, J.: Monte-carlo opening books for amazons. In: van den Herik, H.J., Iida, H., Plaat, A. (eds.) CG 2010. LNCS, vol. 6515, pp. 124–135. Springer, Heidelberg (2011)
Baier, H., Winands, M.H.M.: Active Opening Book Application for Monte-Carlo Tree Search in 19×19 Go. In: 23rd Benelux Conference on Artificial Intelligence (BNAIC 2011), pp. 3–10 (2011)
Wu, I.-C., Lin, H.-H., Lin, P.-H., Sun, D.-J., Chan, Y.-C., Chen, B.-T.: Job-level proof-number search for connect6. In: van den Herik, H.J., Iida, H., Plaat, A. (eds.) CG 2010. LNCS, vol. 6515, pp. 11–22. Springer, Heidelberg (2011)
Wu, I.-C., Lin, H.-H., Sun, D.-J., Kao, K.-Y., Lin, P.-H., Chan, Y.-C., Chen, P.-T.: Job-Level Proof Number Search. IEEE Transactions on Computational Intelligence and AI in Games 5(1), 44–56 (2013)
Saffidine, A., Jouandeau, N., Cazenave, T.: Solving breakthrough with race patterns and job-level proof number search. In: van den Herik, H.J., Plaat, A. (eds.) ACG 2011. LNCS, vol. 7168, pp. 196–207. Springer, Heidelberg (2012)
Chen, J.-C., Wu, I.-C., Tseng, W.-J., Lin, B.-H., Chang, C.-H.: Job-Level Alpha Beta Search. In: IEEE Transactions on Computational Intelligence and AI in Games (in Press, 2014)
Schaeffer, J., Burch, N., Björnsson, Y., Kishimoto, A., Müller, M., Lake, R., Lu, P., Sutphen, S.: Checkers is Solved. Science 317(5844), 1518–1522 (2007)
Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-Time Analysis of the Multiarmed Bandit Problem. Machine Learning 47(2-3), 235–256 (2002)
Browne, C.B., Powley, E., Whitehouse, D., Lucas, S.M., Cowling, P.I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., Colton, S.: A Survey of Monte Carlo Tree Search Methods. IEEE Transactions on Computational Intelligence and AI in Games 4(1), 1–43 (2012)
Lin, P.-H., Wu, I.: NCTU6 Wins Man-Machine Connect6 Championship 2009. ICGA Journal 32(4), 230–232 (2009)
Wei, T.-H., Tseng, W.-J., Wu, I., Yen, S.-J.: Mobile6 Wins Connect6 Tournament. ICGA Journal 36(3), 178–179 (2013)
Wu, I., Lin, Y.-S., Tsai, H.-T., Lin, P.-H.: The Man-Machine Connect6 Championship 2011. ICGA Journal 34(2), 103–105 (2011)
Wu, I.-C., Lin, P.: NCTU6-Lite Wins Connect6 Tournament. ICGA Journal 31(4), 240–243 (2008)
Wu, I.-C., Yen, S.-J.: NCTU6 Wins Connect6 Tournament. ICGA Journal 29(3), 157–158 (2006)
Wu, I.-C., Lin, P.-H.: Relevance-Zone-Oriented Proof Search for Connect6. IEEE Transactions on Computational Intelligence and AI in Games 2(3), 191–207 (2010)
Wu, I.-C., Tsai, H.-T., Lin, H.-H., Lin, Y.-S., Chang, C.-M., Lin, P.-H.: Temporal Difference Learning for Connect6. In: van den Herik, H.J., Plaat, A. (eds.) ACG 2011. LNCS, vol. 7168, pp. 121–133. Springer, Heidelberg (2012)
Allis, L.V., van der Meulen, M., van den Herik, H.J.: Proof-Number Search. Artificial Intelligence 66(1), 91–124 (1994)
Chou, C.-W., Chou, P.-C., Doghmen, H., Lee, C.-S., Su, T.-C., Teytaud, F., Teytaud, O., Wang, H.-M., Wang, M.-H., Wu, L.-W., Yen, S.-J.: Towards a solution of 7x7 go with meta-MCTS. In: van den Herik, H.J., Plaat, A. (eds.) ACG 2011. LNCS, vol. 7168, pp. 84–95. Springer, Heidelberg (2012)
Little Golem, http://www.littlegolem.net
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-14923-3_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14922-6
Online ISBN: 978-3-319-14923-3
eBook Packages: Computer ScienceComputer Science (R0)