Locally Informed Global Search for Sums of Combinatorial Games

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Locally Informed Global Search for Sums of Combinatorial Games
Abstract. There are two complementary approaches to playing sums of combinatorial games. They can be characterized as local analysis and global search. Algorithms from combinatorial game theory such as Hotstrat and Thermostrat [2] exploit summary information about each subgame such as its temperature or its thermograph. These algorithms can achieve good play, with a bounded error. Their runtime depends mainly on the complexity of analyzing individual subgames. Most importantly, it is not exponential in global parameters such as the branching factor and the number of moves in the sum game. One problem of these classic combinatorial game algorithms is that they cannot exploit extra available computation time. A global minimax search method such as can determine the optimal result of a sum game. However, the solution cost grows exponentially with the total size of the sum game, even if each subgame by itself is simple. Such an approach does not exploit the independence of subgames. This ...
Martin Müller 0003, Zhichao Li
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2004
Where CG
Authors Martin Müller 0003, Zhichao Li
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