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Validating and Fine-Tuning of Game Evaluation Functions Using Endgame Databases

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Part of the Communications in Computer and Information Science book series (CCIS,volume 818)


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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  • DOI: 10.1007/978-3-319-75931-9_10
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  1. 1.

    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.


  1. Shredder computer chess:

  2. Lomonosov endgame tablebases. (2017)

  3. 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).

    CrossRef  Google Scholar 

  4. Buro, M.: Improving heuristic mini-max search by supervised learning. Artif. Intell. 134(1–2), 85–99 (2002)

    CrossRef  MATH  Google Scholar 

  5. Chen, B.N., Shen, B.J., Hsu, T.S.: Chinese dark chess. ICGA J. 33(2), 93–106 (2010)

    CrossRef  Google Scholar 

  6. 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)

    Google Scholar 

  7. Hsieh, C.H.: Food-chain realtion’s discussion and implementation in Chinese dark chess. Master’s thesis, National Taiwan Normal University, July 2010

    Google Scholar 

  8. 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)

    Google Scholar 

  9. Knuth, D.E., Moore, R.W.: An analysis of alpha-beta pruning. Artif. Intell. 6(4), 293–326 (1975)

    MathSciNet  CrossRef  MATH  Google Scholar 

  10. 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).

    CrossRef  Google Scholar 

  11. 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)

    MathSciNet  CrossRef  MATH  Google Scholar 

  12. Shannon, C.E.: Programming a computer for playing chess. In: Levy, D. (ed.) Computer Chess Compendium, pp. 2–13. Springer, New York (1988)

    CrossRef  Google Scholar 

  13. Strang, G.: Introduction to Linear Algebra. Wellesley-Cambridge Press, Wellesley (2016)

    MATH  Google Scholar 

  14. Thompson, K.: Retrograde analysis of certain endgames. ICCA J. 9(3), 131–139 (1986)

    MathSciNet  Google Scholar 

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Correspondence to Tsan-sheng Hsu .

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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.

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  • Print ISBN: 978-3-319-75930-2

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