Auflistung nach:
Auflistung Künstliche Intelligenz 25(1) - März 2011 nach Schlagwort "General game playing"
1 - 8 von 8
Treffer pro Seite
Sortieroptionen
- ZeitschriftenartikelA GGP Feature Learning Algorithm(KI - Künstliche Intelligenz: Vol. 25, No. 1, 2011) Kirci, Mesut; Sturtevant, Nathan; Schaeffer, JonathanThis paper presents a learning algorithm for two-player, alternating move GGP games. The Game Independent Feature Learning algorithm, GIFL, uses the differences in temporally-related states to learn patterns that are correlated with winning or losing a GGP game. These patterns are then used to inform the search. GIFL is simple, robust and improves the quality of play in the majority of games tested. GIFL has been successfully used in the GGP program Maligne.
- ZeitschriftenartikelA Parallel General Game Player(KI - Künstliche Intelligenz: Vol. 25, No. 1, 2011) Méhat, Jean; Cazenave, TristanWe have parallelized our general game player Ary on a cluster of computers. We propose multiple parallelization algorithms. For the sake of simplicity all our algorithms have processes that run independently and that join their results at the end of the thinking time in order to choose a move. Parallelization works very well for checkers, quite well for other two player sequential move games and not at all for a few other games.
- ZeitschriftenartikelCadiaPlayer: Search-Control Techniques(KI - Künstliche Intelligenz: Vol. 25, No. 1, 2011) Finnsson, Hilmar; Björnsson, YngviEffective search control is one of the key components of any successful simulation-based game-playing program. In General Game Playing (GGP), learning of useful search-control knowledge is a particularly challenging task because it must be done in real-time during online play. In here we describe the search-control techniques used in the 2010 version of the GGP agent CadiaPlayer, and show how they have evolved over the years to become increasingly effective and robust across a wide range of games. In particular, we present a new combined search-control scheme (RAVE/MAST/FAST) for biasing action selection. The scheme proves quite effective on a wide range of games including chess-like games, which have up until now proved quite challenging for simulation-based GGP agents.
- ZeitschriftenartikelCenturio, a General Game Player: Parallel, Java- and ASP-based(KI - Künstliche Intelligenz: Vol. 25, No. 1, 2011) Möller, Maximilian; Schneider, Marius; Wegner, Martin; Schaub, TorstenWe present the General Game Playing system Centurio. Centurio is a Java-based player featuring different strategies based on Monte Carlo Tree Search extended by techniques borrowed from Upper Confidence bounds applied to Trees as well as Answer Set Programming (for single-player games). Centurio’s Monte Carlo Tree Search is accomplished in a massively parallel way by means of multi-threading as well as cluster-computing. Another major feature of Centurio is its compilation of game descriptions, states, and state manipulations into Java, yielding an edge over existing Prolog-based approaches. Centurio is open source software freely available via the web.
- ZeitschriftenartikelGamer, a General Game Playing Agent(KI - Künstliche Intelligenz: Vol. 25, No. 1, 2011) Kissmann, Peter; Edelkamp, StefanThis work is concerned with our general game playing agent Gamer. In contrast to many other players, we do not only use a Prolog-like mechanism to infer knowledge about the current state and the available moves but instantiate the games to reduce the inference time in parallel UCT game tree search. Furthermore, we use the generated output to try to solve the games using symbolic search methods and thus play optimally.
- ZeitschriftenartikelGDL-II(KI - Künstliche Intelligenz: Vol. 25, No. 1, 2011) Thielscher, MichaelThe Game Description Language (GDL) used in the past AAAI competitions allows to tell a system the rules of arbitrary finite games that are characterised by perfect information, but does not extend to games in which players have asymmetric information, e.g. about their own hand of cards, or which involve elements of chance like the roll of dice. Accordingly, contemporary general game-playing systems are not designed to play games such as Backgammon, Poker or Diplomacy. GDL-II (for: GDL with Incomplete/Imperfect Information) is a recent extension of the original description language that makes general game playing truly general, because it allows to describe just any finite game with arbitrary forms of randomness as well as imperfect/incomplete information. This brings along the challenge to build the next generation of truly general game-playing systems that are able to understand any game description given in GDL-II and to learn to master these types of games, too.
- ZeitschriftenartikelKnowledge-Based General Game Playing(KI - Künstliche Intelligenz: Vol. 25, No. 1, 2011) Haufe, Sebastian; Michulke, Daniel; Schiffel, Stephan; Thielscher, MichaelAlthough 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.
- ZeitschriftenartikelUsing Decision Trees for State Evaluation in General Game Playing(KI - Künstliche Intelligenz: Vol. 25, No. 1, 2011) Sheng, Xinxin; Thuente, DavidA general game playing agent understands the formal descriptions of an arbitrary game in the multi-agent environment and learns to play the given games without human intervention. In this paper, we present an agent that automatically extracts common features shared by the game winners and uses such learned features to build decision trees to guide the heuristic search. We present data to show the significant performance improvements contributed by the decision tree evaluation. We also show by using hash tables in knowledge reasoning, our agent uses 80% less time when compared to a widely available GGP agent written in the same language.