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A Small Go Board Study of Metric and Dimensional Evaluation Functions

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2883))

Abstract

The difficulty of writing successful 19 × 19 Go programs lies not only in the combinatorial complexity of Go but also in the complexity of designing a good evaluation function containing a lot of knowledge. Leaving these obstacles aside, this paper defines very-little-knowledge evaluation functions used by programs playing on very small boards. The evaluation functions are based on two mathematical tools, distance and dimension, and not on domain-dependent knowledge. After a qualitative assessment of each evaluation function, we built several programs playing on 4 × 4 boards by using tree search associated with these evaluation functions. We set up an experiment to select the best programs and identify the relevant features of these evaluation functions. From the results obtained by these very-little-knowledge-based programs, we can judge the usefulness of each evaluation function.

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References

  1. Chen, K.: Computer Go: Knowledge, search, and move decision. International Computer Chess Association Journal 24, 203–215 (2001)

    Google Scholar 

  2. Bouzy, B.: Go patterns generated by retrograde analysis. In: Uiterwijk, J. (ed.) The 6th Computer Olympiad Computer-Games Workshop Proceedings (2001) Report CS 01-04

    Google Scholar 

  3. Bouzy, B., Cazenave, T.: Computer Go: an AI oriented survey. Artificial Intelligence 132, 39–103 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  4. Mandelbrot, B.: The fractal geometry of nature. W.H. Freeman and Company, San Francisco (1982)

    MATH  Google Scholar 

  5. van Rijswijk, J.: Computer Hex: are bees better than fruitflies? Master’s thesis, University of Alberta, Edmonton, AB (2000)

    Google Scholar 

  6. Enzenberger, M.: The integration of a priori knowledge into a Go playing neural network, http://www.markus-enzenberger.de/neurogo.html (1996)

  7. Thorp, E., Walden, W.: A computer assisted study of Go on MxN boards. Information Sciences 4, 1–33 (1972)

    MathSciNet  Google Scholar 

  8. Lorentz, R.: 2xN Go. In: Proceedings of the 4th Game Programming Workshop in Japan 1997, pp. 65–74 (1997)

    Google Scholar 

  9. Sei, S., Kawashima, T.: A solution of Go on 4×4 board by game tree search program. Fujitsu Social Science Laboratory manuscript (2000)

    Google Scholar 

  10. Slate, D., Atkin, L.: Chess 4.5 – the Northwestern University chess program. In: Frey, P. (ed.) Chess Skill in Man and Machine, pp. 82–118. Springer, Heidelberg (1977)

    Google Scholar 

  11. Greenblatt, R., Eastlake III, D., Crocker, S.: The Greenblatt chess program. In: Fall Joint Computing Conference 31, pp. 801–810. ACM, New-York (1967)

    Google Scholar 

  12. Marsland, T.: A review of game-tree pruning. International Computer Chess Association Journal 9, 3–19 (1986)

    Google Scholar 

  13. Schaeffer, J.: The history heuristic and alpha-beta search enhancements in practice. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 1203–1212 (1989)

    Article  Google Scholar 

  14. Donninger, C.: Null move and deep search: selective-search heuristics for obtuse chess programs. International Computer Chess Association Journal 16, 137–143 (1993)

    Google Scholar 

  15. Plaat, A., Schaeffer, J., Pijls, W., De Bruin, A.: Best-first fixed-depth minimax algorithms. Artificial Intelligence 87, 255–293 (1996)

    Article  MathSciNet  Google Scholar 

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© 2003 Springer-Verlag Berlin Heidelberg

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Bouzy, B. (2003). A Small Go Board Study of Metric and Dimensional Evaluation Functions. In: Schaeffer, J., Müller, M., Björnsson, Y. (eds) Computers and Games. CG 2002. Lecture Notes in Computer Science, vol 2883. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-40031-8_25

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  • DOI: https://doi.org/10.1007/978-3-540-40031-8_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20545-6

  • Online ISBN: 978-3-540-40031-8

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