No description available.

Domain-independent feature learning is a hard problem. This is reflected by lack of broad research in the area. The goal of General Game Playing (GGP) can be described as designing computer programs that can play a variety of games given only a logical game description. Any learning has to be domain-independent in the GGP framework. Learning algorithms have not been an essential part of all successful GGP programs. This thesis presents a feature learning approach, GIFL, for 2-player, alternating move games using state differences. The algorithm is simple, robust and improves the quality of play. GIFL is implemented in a GGP program, Maligne. The experiments show that GIFL outperforms standard UCT algorithm in nine out of fifteen games and loses performance only in one game.

No description available.

No description available.

Computer Game Playing has been an active area of research since Samuel’s first Checkers player (Samuel 1959). Recently interest beyond the classic games of Chess and Checkers has led to competitions such as the General Game Playing competition, in which players have no beforehand knowledge of the games they are to play, and the Computer Poker Competition which force players to reason about imperfect information under conditions of uncertainty. The purpose of this dissertation is to explore the area of General Game Playing both specifically and generally. On the specific side, we describe the design and implementation of our General Game Playing system OGRE. This system includes an innovative method for feature extraction that helped it to achieve second and fourth place in two international General Game Playing competitions. On the more general side, we also introduce the Regular Game Language, which goes beyond current works to provide support for both stochastic and imperfect information games as well as the more traditional games.

Computer-based training systems have become a mainstay in military and private institutions for training people how to perform certain complex tasks. As these tasks expand in difficulty, intelligent agents will appear as virtual teammates or tutors assisting a trainee in performing and learning the task. For developing these agents, we must obtain the strategies from expert players and emulate their behavior within the agent. Past researchers have shown the challenges in acquiring this information from expert human players and translating it into the agent. A solution for this problem involves using computer systems that assist in the human expert knowledge elicitation process. In this thesis, we present an approach for developing an agent for the game Revised Space Fortress, a game representative of the complex tasks found in training systems. Using machine learning techniques, the agent learns the strategy for the game by observing how a human expert plays. We highlight the challenges encountered while designing and training the agent in this real-time game environment, and our solutions toward handling these problems. Afterward, we discuss our experiment that examines whether trainees experience a difference in performance when training with a human or virtual partner, and how expert agents that express distinctive behaviors affect the learning of a human trainee. We show from our results that a partner agent that learns its strategy from an expert player serves the same benefit as a training partner compared to a programmed expert-level agent and a human partner of equal intelligence to the trainee.

Thesis (M.Eng.). / Written for the Dept. of Electrical and Computer Engineering. Title from title page of PDF (viewed 2008/05/13). Includes bibliographical references.

Dans cette thèse nous présentons une approche générale et robuste pour la décomposition des jeux décrits en Game Description Language (GDL). Dans le domaine du General Game Playing (GGP), les joueurs peuvent significativement diminuer le coût de l'exploration d'un jeu s'ils disposent d'une version décomposée de celui-ci. Les travaux existants sur la décomposition des jeux s'appuient sur la structure syntaxique des règles, sur des habitudes d'écriture du GDL ou sur le coûteux calcul de la forme normale disjonctive des règles. Nous proposons une méthode plus générale pour décomposer les jeux solitaires ou multijoueurs. Dans un premier temps nous envisageons une approche fondée sur l'analyse logique des règles. Celle-ci nécessite l'utilisation d'heuristiques, qui en limitent la robustesse, et le coûteux calcul de la forme normale disjonctive des règles. Une seconde approche plus efficace est fondée sur la collecte d'informations durant des simulations (playouts). Cette dernière permet la détection des liens de causalité entre les actions et les fluents d'un jeu. Elle est capable de traiter les différents types de jeux composés et de prendre en charge certains cas difficiles comme les jeux à actions composées et les jeux en série. Nous avons testé notre approche sur un panel de 597 jeux GGP. Pour 70% des jeux, la décomposition nécessite moins d'une minute en faisant 5k playouts. Nous montrons de 87% d'entre eux peuvent être correctement décomposés après seulement 1k playouts. Nous ébauchons également une approche originale pour jouer avec ces jeux décomposés. Les tests préliminaires sur quelques jeux solitaires sont prometteurs. / This PhD thesis presents a robust and general approach for the decomposition of games described in Game Description Language (GDL). In the General Game Playing framework (GGP), players can drastically decrease game search cost if they hold a decomposed version of the game. Previous works on decomposition rely on syntactical structures and writing habits of the GDL, or on the disjunctive normal form of the rules, which is very costly to compute. We offer an approach to decompose single or multi-player games. First, we consider an approach based on logical rule analysis. This requires the use of heuristics, which limit its robustness, and the costly calculation of the disjunctive normal form of the rules. A second more efficient approach is based on information gathering during simulations of the game (playouts). The latter allows the detection of causal links between actions. It can handle the different classes of compound games and can process some difficult cases like synchrounous parallel games with compound moves and serial games. We tested our program on 597 games. Given 5k playouts, 70% of the games are decomposed in less than one minute. We demonstrate that for 87% of the games, 1k playouts are sufficient to obtain a correct decomposition. We also sketch an original approach to play with these decomposed games. Preliminary tests on some one-player games are promising. Another contribution of this thesis is the evaluation of the MPPA architecture for the parallelization of a GGP player (LeJoueur of Jean-Noël Vittaut).

Dataspelsproblematik är ett eskalerande problem som har visat sig påverka många unga människors utveckling och välbefinnande. Syftet med denna studie var därför att undersöka ungas dataspelsproblematik, vilka konsekvenser ett överdrivet dataspelande medför samt vilken behandling som ges till unga med dataspelsproblematik. Empirin erhölls genom kvalitativa intervjuer med professionella som arbetar med denna typ av problematik vid verksamheterna Game Over och Theory in Action. Resultatet visade att dataspelsproblematik idag inte har en bestämd definition och att det är ett problem som inte är tillräckligt uppmärksammat. Synen på orsaker till dataspelsproblematik hos unga är komplext då det kan tänkas bero på många olika faktorer. Konsekvenserna av ett problematiskt dataspelande är många och allvarliga och kan påverka den unges hela livssituation. Behandling av unga med dataspelsproblematik har visats sig ske på varierande sätt med olika utgångspunkter beroende på vad man anser är orsaken till problematiken. / Computer game related problems are an escalating problem that has been shown to affect many young people's development and well-being. The purpose of this study was to investigate young people's computer game problems, the consequences of excessive computer game playing and the treatment that is given to young people with computer game related problems. The empirical data were obtained through interviews with professionals who work with this type of problem in the institutions Game Over and Theory in Action. The results showed that computer game related problems do not today have a specific definition, as well as it is a problem that is not sufficiently recognized. The view of the causes of young people’s computer game related problems is complex as it may be caused by many different factors. The consequences of a problematic computer game playing are many and serious, which can affect a young person's entire life situation. Treatment of young people with computer game related problems have been shown to occur in various ways with different starting points depending on what you believe is the cause of the problem.

General Game Playing (GGP) deals with the design of players that are able to play any discrete, deterministic, complete information games. For many games like chess, designers develop a player using a specially designed algorithm and tune all the features of the algorithm to play the game as good as possible. However, a general game player knows nothing about the game that is about to be played. When the game begins, game description is given to the players and they should analyze it and decide on the best way to play the game. In this thesis, we focus on two-player constant-sum simultaneous move games in GGP and how this class of games can be handled. Rock-paper-scissors can be considered as a typical example of a simultaneous move game. We introduce the CFR algorithm to the GGP community for the first time and show its effectiveness in playing simultaneous move games. This is the first implementation of CFR outside the poker world. We also improve the UCT algorithm, which is the state of the art in GGP, to be more robust in simultaneous move games. In addition, we analyze how UCT performs in simultaneous move games and argue that it does not converge to a Nash equilibrium. We also compare the usage of UCT and CFR in this class of games. Finally, we discuss about the importance of opponent modeling and how a model of the opponent can be exploited by using CFR.