Abstract
We apply Monte-Carlo simulation and alpha-beta search to the card game of Skat, which is similar to Bridge, but sufficiently different to require some new algorithmic ideas besides the techniques developed for Bridge. Our Skat-playing program, called DDS (Double Dummy Solver), integrates well-known techniques such as move ordering with two new search enhancements. Quasi-symmetry reduction generalizes symmetry reductions, disseminated by Ginsberg’s Partition Search algorithm, to search states which are “almost equivalent”. Adversarial heuristics generalize ideas from single-agent search algorithms like \(\textrm{A}^*\) to two-player games, leading to guaranteed lower and upper bounds for the score of a game position. Combining these techniques with state-of-the-art tree-search algorithms, our program determines the game-theoretical value of a typical Skat hand (with perfect information) in 10 milliseconds.
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Kupferschmid, S., Helmert, M. (2007). A Skat Player Based on Monte-Carlo Simulation. In: van den Herik, H.J., Ciancarini, P., Donkers, H.H.L.M.(. (eds) Computers and Games. CG 2006. Lecture Notes in Computer Science, vol 4630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75538-8_12
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DOI: https://doi.org/10.1007/978-3-540-75538-8_12
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