Searching with partial belief states in general games with incomplete information

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. © 2012 Springer-Verlag.