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For general two-player turn-taking games, first solvers have been contributed. Algorithms for multi-player games like Maxn, however, cannot classify general games robustly, and its extension Soft-Maxn, which can play optimally against unknown and weak opponents, demands large amounts of memory. As RAM is a scarce resource, this paper proposes a memory-efficient implementation of the Soft-Maxn algorithm, by exploiting the functional representation of state and evaluation sets with BDDs.
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