Searching with Partial Belief States in General Games with Incomplete Information
作者： Stefan Edelkamp , Tim Federholzner , Peter Kissmann
DOI: 10.1007/978-3-642-33347-7_3
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摘要: In this paper we present a full-fledged player for general games with incomplete information specified in the game description language GDL-II. To deal uncertainty introduce method that operates on partial belief states, which correspond to subset of set states building full state. search state depth-first and Monte-Carlo methods. All can be combined any traditional player, e.g., using minimax or UCT search. Our is shown effective number benchmarks variant compares positively one-and-only winner an track at international playing competition.
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