Abstract
The evaluation function and search algorithm are the two main components of almost all game playing programs. A good evaluation function is carefully designed to assess a position by considering the location and the material value of all pieces on board. Normally, an evaluation function f is manually designed, which requires a large amount of expert knowledge. Usually, f must be able to evaluate any position. Theoretically, a huge table that stores all the pre-computed scores for every position can perfectly represent any position. However, it is space-efficient to encode f, which is far from being perfect. On the other hand, endgame databases provide game theoretical values for all legal positions when the total number of pieces remains is small, say within 5 or 6 for Chinese dark chess (CDC). However, only a selected number of endgame databases are available. Furthermore, the size of an endgame database is huge, e.g., from megabytes to gigabytes. We construct a scheme to use the information from endgame databases to validate and fine-tune a manually designed evaluation function. Our method abstracts critical information from a huge database and then validates f on positions when they are contained in an endgame database. Using this information, we then discover meta knowledge to fine-tune and revise f so that f better evaluates a position even when f is fed with positions containing many pieces. Experimental results show that our approach is successful.
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1st place in TAAI2013, TCGA2014, TAAI2014 and TAAI2015; 2nd place in TCGA2015, 18th Computer Olympiad, TCGA2016 and 19th Computer Olympiad; 3rd place in 17th Computer Olympiad and TAAI2016.
References
Shredder computer chess: http://www.shredderchess.com/online-chess/online-databases/endgame-database.html
Lomonosov endgame tablebases. http://chessok.com/?page_id=27966 (2017)
Bouzy, B., Helmstetter, B.: Monte-Carlo go developments. In: Van Den Herik, H.J., Iida, H., Heinz, E.A. (eds.) Advances in Computer Games, pp. 159–174. Springer, Boston (2004). https://doi.org/10.1007/978-0-387-35706-5_11
Buro, M.: Improving heuristic mini-max search by supervised learning. Artif. Intell. 134(1–2), 85–99 (2002)
Chen, B.N., Shen, B.J., Hsu, T.S.: Chinese dark chess. ICGA J. 33(2), 93–106 (2010)
Chen, J.C., Lin, T.Y., Hsu, S.C., Hsu, T.S.: Design and implementation of computer Chinese dark chess endgame database. In: Proceeding of TCGA Workshop, pp. 5–9 (2012)
Hsieh, C.H.: Food-chain realtion’s discussion and implementation in Chinese dark chess. Master’s thesis, National Taiwan Normal University, July 2010
Hsu, T.S., Hsu, S.C., Che, J.C., Chiang, Y.T., Chen, B.N., Liu, Y.C., Chang, H.J., Tsai, S.C., Lin, T.Y., Fan, G.Y.: Computers and Classical Board Games: An Introduction. National Taiwan University Press, Taiwan (2017)
Knuth, D.E., Moore, R.W.: An analysis of alpha-beta pruning. Artif. Intell. 6(4), 293–326 (1975)
Kocsis, L., Szepesvári, C.: Bandit based Monte-Carlo planning. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 282–293. Springer, Heidelberg (2006). https://doi.org/10.1007/11871842_29
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)
Shannon, C.E.: Programming a computer for playing chess. In: Levy, D. (ed.) Computer Chess Compendium, pp. 2–13. Springer, New York (1988)
Strang, G.: Introduction to Linear Algebra. Wellesley-Cambridge Press, Wellesley (2016)
Thompson, K.: Retrograde analysis of certain endgames. ICCA J. 9(3), 131–139 (1986)
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Chang, HJ., Fan, GY., Chen, JC., Hsueh, CW., Hsu, Ts. (2018). Validating and Fine-Tuning of Game Evaluation Functions Using Endgame Databases. In: Cazenave, T., Winands, M., Saffidine, A. (eds) Computer Games. CGW 2017. Communications in Computer and Information Science, vol 818. Springer, Cham. https://doi.org/10.1007/978-3-319-75931-9_10
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