By Chrilly Donninger, Ulf Lorenz (auth.), H. Jaap van den Herik, Shun-Chin Hsu, Tsan-sheng Hsu, H. H. L. M. (Jeroen) Donkers (eds.)
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Additional info for Advances in Computer Games: 11th International Conference, ACG 2005, Taipei, Taiwan, September 6-9, 2005. Revised Papers
Sutphen. Solving Checkers. In International Joint Conference on Artificial Intelligence (IJCAI), pages 292–297, 2005. 7. J. Schaeﬀer, Y. Bjornsson, Neil Burch, Robert Lake, Paul Lu, and Steve Sutphen. Building the 10-piece Checkers Endgame Databases. In 10th Advances in Computer Games (ACG10), Many Games, Many Challenges (eds. H. J. van den Herik, H. Iida, and E. A. Heinz), Kluwer Academic Publishers, Boston, pages 193–210, 2004. 8. T. Str¨ ohlein. Untersuchungen uber Kombinatorische Spiele. Dissertation, Fakult¨ at f¨ ur Allgemeine Wissenschaften der Technischen Hochschule M¨ unchen, 1970.
The use of parameter optimization methods for the performance tuning of game programs is diﬃcult by the fact that the objective function is rarely available analytically. Therefore, methods that rely on the availability of an analytic expression for the gradient cannot be used. However, there exist several ways to tune parameters despite the lack of an analytic gradient. An important class of such algorithms is represented by temporal-diﬀerence (TD) methods that have been used successfully in tuning evaluation-function parameters .
Thus, even the current version of Pilot is able to eﬀectively perceive these subtle diﬀerences. What currently lacks is a mechanism for explicitly testing such conﬁgurations. Finally, Pilot has the potential to generate a new search engine on a per-evaluation-function basis. Current results have used minimalist evaluation functions, and it will be extremely interesting to see what is possible with an evaluation function that has been tuned for a speciﬁc game. The ultimate goal is to free humans from the drudgery of creating a new search engine for every new game that comes along.