Abstract
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 with uncertainty we introduce a method that operates on partial belief states, which correspond to a subset of the set of states building a full belief state. To search for a partial belief state we present depth-first and Monte-Carlo methods. All can be combined with any traditional general game player, e.g., using minimax or UCT search.
Our general game player is shown to be effective in a number of benchmarks and the UCT variant compares positively with the one-and-only winner of an incomplete information track at an international general game playing competition.
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Edelkamp, S., Federholzner, T., Kissmann, P. (2012). Searching with Partial Belief States in General Games with Incomplete Information. In: Glimm, B., Krüger, A. (eds) KI 2012: Advances in Artificial Intelligence. KI 2012. Lecture Notes in Computer Science(), vol 7526. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33347-7_3
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DOI: https://doi.org/10.1007/978-3-642-33347-7_3
Publisher Name: Springer, Berlin, Heidelberg
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