A program to play Kriegspiel

Proceedings of the Twentieth International Joint …

… Intelligence and Games (CIG), 2010

2009

Monte Carlo tree search has brought significant improvements to the level of computer players in games such as Go, but so far it has not been used very extensively in games of strongly imperfect information with a dynamic board and an emphasis on risk management and decision making under uncertainty. In this paper we explore its application to the game of Kriegspiel (invisible chess), providing three Monte Carlo methods of increasing strength for playing the game with little specific knowledge. We compare these Monte Carlo agents to the strongest known minimax-based Kriegspiel player, obtaining significantly better results with a considerably simpler logic and less domain-specific knowledge. 1

Computational Intelligence, 1996

Science of Computer Programming, 2007

Künstliche Intelligenz, 2011

Although we humans cannot compete with computers at simple brute-force search, this is often more than compensated for by our ability to discover structures in new games and to quickly learn how to perform highly selective, informed search. To attain the same level of intelligence, general game playing systems must be able to figure out, without human assistance, what a new game is really about. This makes General Game Playing in ideal testbed for human-level AI, because ultimate success can only be achieved if computers match our ability to master new games by acquiring and exploiting new knowledge. This article introduces five knowledge-based methods for General Game Playing. Each of these techniques contributes to the ongoing success of our FLUXPLAYER (Schiffel and Thielscher in Proceedings of the National Conference on Artificial Intelligence, pp. 1191–1196, 2007), which was among the top four players at each of the past AAAI competitions and in particular was crowned World Champion in 2006.

Preprint, 2009

Artificial Intelligence, 2002

1993

An information set in a game tree is a set of nodes from which the rules of the game require that the same alternative (i.e., move) be selected. Thus the nodes an information set are indistinguishable to the player moving from that set, thereby reflecting imperfect in- formation, that is, information hidden from that player. Information sets arise naturally in (for example) card gaines like poker and bridge. IIere we focus not on the solution concept for im- perfect information games (which has been studied at length), but rather on the computational aspects of such games: how hard is it to compute solutions? We present two fundainental results for imperfect informa- tion games. The first result shows that even if there is only a single player, we must seek special cases or heuristics. The second result complements the first, providing an efficient algorithm for just such a special case. Additionally, we show how our special case algo- rithm can be used as a heuristic in the general...

Lecture Notes in Computer Science, 1999