Literature

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    AuthorTitleYearJournal/ProceedingsReftypeDOI/URL
    Asgharbeygi, N., Stracuzzi, D. & Langley, P. Relational Temporal Difference Learning

    [Abstract] [BibTeX]

    		 2006 International Conference

    on Machine Learning 

    		 inproceedings URL 
    Abstract: We introduce relational temporal difference learning as an effective approach to solving multi-agent Markov decision problems with large state spaces. Our algorithm uses temporal difference reinforcement to learn a distributed value function represented over a conceptual hierarchy of relational predicates. We present experiments using two domains from the General Game Playing repository, in which we observe that our system achieves higher learning rates than non-relational methods. We also discuss related work and directions for future research.
    BibTeX: 
    @inproceedings{Asgharbeygi2006,
  author = {Nima Asgharbeygi and David Stracuzzi and Pat Langley},
  title = {Relational Temporal Difference Learning},
  booktitle = {International Conference
    on Machine Learning},
  year = {2006},
  url = {http://www.icml2006.org/icml_documents/camera-ready/007_Relational_Temporal.pdf}
}
    Banerjee, B., Kuhlmann, G. & Stone, P. Value Function Transfer for General Game Playing

    [Abstract] [BibTeX]

    		 2006 ICML workshop on Structural Knowledge Transfer for Machine Learning  inproceedings URL 
    Abstract: We present value function transfer techniques for General Game Playing (GGP) by Reinforcement Learning. We focus on 2 player, alternate-move, complete information board games and use the GGP simulator and framework. Our approach is two-pronged: first we extract knowledge about crucial regions in the value-function space of any game in the genre. Then for each target game, we generate a smaller version of this game and extract symmetry information from the board setup. The combined knowledge of value function and symmetry allows us to achieve significant transfer via Reinforcement Learning, to larger board games using only a limited size of state-space by virtue of exploiting symmetry.
    BibTeX: 
    @inproceedings{Banerjee2006,
  author = {Bikramjit Banerjee and Gregory Kuhlmann and Peter Stone},
  title = {Value Function Transfer for General Game Playing},
  booktitle = {ICML workshop on Structural Knowledge Transfer for Machine Learning},
  year = {2006},
  url = {http://www.cs.utexas.edu/~pstone/Papers/bib2html-links/ICML06-bikram.pdf}
}
    Banerjee, B. & Stone, P. General Game Learning using Knowledge Transfer

    [Abstract] [BibTeX]

    		 2007 The 20th International Joint Conference on Artificial Intelligence, pp. 672-677  inproceedings URL 
    Abstract: We present a reinforcement learning game player that can interact with a General Game Playing system and transfer knowledge learned in one game to expedite learning in many other games. We use the technique of value-function transfer where general features are extracted from the state space of a previous game and matched with the completely different state space of a new game. To capture the underlying similarity of vastly disparate state spaces arising from different games, we use a game-tree lookahead structure for features. We show that such feature-based value function transfer learns superior policies faster than a reinforcement learning agent that does not use knowledge transfer. Furthermore, knowledge transfer using lookahead features can capture opponent-specific value-functions, i.e. can exploit an opponent's weaknesses to learn faster than a reinforcement learner that uses lookahead with minimax (pessimistic) search against the same opponent.
    BibTeX: 
    @inproceedings{Banerjee2007,
  author = {Bikramjit Banerjee and Peter Stone},
  title = {General Game Learning using Knowledge Transfer},
  booktitle = {The 20th International Joint Conference on Artificial Intelligence},
  year = {2007},
  pages = {672--677},
  url = {http://www.cs.utexas.edu/~pstone/Papers/bib2html-links/IJCAI07-bikram.pdf}
}
    Chaslot, G., Hoock, J., Perez, J., Rimmel, A., Teytaud, O. & Winands, M. Meta Monte-Carlo Tree Search for Automatic Opening Book Generation

    [BibTeX]

    		 2009 Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09)  inproceedings  
    BibTeX: 
    @inproceedings{Chaslot2009,
  author = {Guillaume Chaslot and Jean-Baptiste Hoock and Julien Perez and Arpad Rimmel and
    Olivier Teytaud and Mark Winands},
  title = {Meta Monte-Carlo Tree Search for Automatic Opening Book Generation},
  booktitle = {Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09)},
  year = {2009}
}
    Clune, J. Heuristic Evaluation Functions for General Game Playing

    [Abstract] [BibTeX]

    		 2007 AAAI, pp. 1134-1139  inproceedings URL 
    Abstract: A general game playing program plays games that it has not previously encountered. A game manager program sends the game playing programs a description of a game's rules and objectives in a well-defined game description language. A central challenge in creating effective general game playing programs is that of constructing heuristic evaluation functions from game descriptions. This paper describes a method for constructing evaluation functions that represent exact values of simplified games. The simplified games are abstract models that incorporate the most essential aspects of the original game, namely payoff, control, and termination. Results of applying this method to a sampling of games suggest that heuristic evaluation functions based on our method are both comprehensible and effective.
    BibTeX: 
    @inproceedings{Clune2007,
  author = {James Clune},
  title = {Heuristic Evaluation Functions for General Game Playing},
  booktitle = {AAAI},
  publisher = {AAAI Press},
  year = {2007},
  pages = {1134--1139},
  url = {http://moodle.cs.ualberta.ca/file.php/49/AAAI07-180.pdf}
}
    Cox, E., Schkufza, E., Madsen, R. & Genesereth, M. Factoring General Games using Propositional Automata

    [Abstract] [BibTeX]

    		 2009 Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09)  inproceedings URL 
    Abstract: In this paper we propose the design of a more robust General Game Player, able to successfully play the class of synchronous independent games using Propositional Automata, a framework for reasoning about discrete, dynamic, multiagent systems, developed by the Stanford Logic Group. We prove conditions under which a Propositional Automata represents multiple, synchronous independent subgames, and in doing so, prove when a Propositional Automata can be separated, or factored into multiple automata. A General Game Player able to recognize such independences can computationally save, by searching the smaller game trees represented by the independent factored automata for solutions. Additionally we explore the concept of independent substructures appearing within Propositional Automata given certain states, and prove conditions under which a Propositional Automata is contingently factorable into multiple Propositional Automata.
    BibTeX: 
    @inproceedings{Cox2009,
  author = {Evan Cox and Eric Schkufza and Ryan Madsen and Michael Genesereth},
  title = {Factoring General Games using Propositional Automata},
  booktitle = {Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09)},
  year = {2009},
  url = {http://logic.stanford.edu/classes/cs227/readings/factoring.pdf}
}
    Edelkamp, S. & Kissmann, P. Symbolic Classification of General Two-Player Games

    [Abstract] [BibTeX]

    		 2008 German Conference on Artificial Intelligence (KI), pp. 185-192  inproceedings URL 
    Abstract: In this paper we present a new symbolic algorithm for the classification, i. e. the calculation of the rewards for both players in case of optimal play, of two-player games with general rewards according to the Game Description Language. We will show that it classifies all states using a linear number of images concerning the depth of the game graph. We also present an extension that uses this algorithm to create symbolic endgame databases and then performs UCT to find an estimate for the classification of the game.
    BibTeX: 
    @inproceedings{Edelkamp2008,
  author = {Stefan Edelkamp and Peter Kissmann},
  title = {Symbolic Classification of General Two-Player Games},
  booktitle = {German Conference on Artificial Intelligence (KI)},
  year = {2008},
  pages = {185-192},
  url = {http://www.tzi.de/~edelkamp/publications/conf/ki/EdelkampK08-1.pdf}
}
    Edelkamp, S. & Kissmann, P. Symbolic Classification of General Multi-Player Games

    [Abstract] [BibTeX]

    		 2008 European Conference on Artificial Intelligence (ECAI), pp. 905-906  inproceedings  
    Abstract: For general two-player turn-taking games, first solvers have been contributed. Algorithms for multi-player games like Maxn, however, cannot classify general games robustly, and its extension Soft-Maxn, which can play optimally against unknown and weak opponents, demands large amounts of memory. As RAM is a scarce resource, this paper proposes a memory-efficient implementation of the Soft-Maxn algorithm, by exploiting the functional representation of state and evaluation sets with BDDs.
    BibTeX: 
    @inproceedings{Edelkamp2008a,
  author = {Stefan Edelkamp and Peter Kissmann},
  title = {Symbolic Classification of General Multi-Player Games},
  booktitle = {European Conference on Artificial Intelligence (ECAI)},
  year = {2008},
  pages = {905-906}
}
    Edelkamp, S. & Kissmann, P. Symbolic Exploration for General Game Playing in PDDL

    [Abstract] [BibTeX]

    		 2007 Workshop on Planning and Games (PG), International Conference on Automated Planning and Scheduling  inproceedings URL 
    Abstract: In artificial intelligence (AI), two- and multiplayer games have been of some interest, but the best AI algorithms always had considerable knowledge of the game they were designed for (Schaeffer 2000). In general game playing (Love, Hinrichs, & Genesereth 2006) strategies are computed domain independently without knowing which game is played. Best policies result in perfect play. The opponents attempt to maximize their gain. Game trees are often depth bounded and values at the leaf nodes of the trees are computed by a static evaluation function. On the other hand, retrograde analysis (Schaeffer et al. 2005) calculates databases of classified positions in backward direction, starting from won and lost ones. These endgame databases can be used in conjunction with game playing programs to eventually solve the game by computing the game theoretical status of the initial position. The purpose of this paper is to close the gap between general game playing and domain independent action planning. Moreover, we illustrate how symbolic planning technology can be applied.
    BibTeX: 
    @inproceedings{Edelkamp2007,
  author = {Stefan Edelkamp and Peter Kissmann},
  title = {Symbolic Exploration for General Game Playing in PDDL},
  booktitle = {Workshop on Planning and Games (PG), International Conference on Automated Planning and Scheduling},
  year = {2007},
  url = {http://www.plg.inf.uc3m.es/icaps-pg2007/papers/Symbolic%20Exploration%20for%20Generalized%20Game%20Playing%20in%20PDDL.pdf}
}
    Edin, E. Classification and Solutions of General Combinatorial Games

    [Abstract] [BibTeX]

    		 2007 School: Royal Institute of Technology, Stockholm, Sweden  mastersthesis URL 
    Abstract: This master's project is an attempt to enable automatic classification and solving of combinatorial games, in the context of general game playing. The Game Description Language is used as the framework for general game playing. The classification of the combinatorial games is focused on the basic properties normal or misère form, all small games, partial or impartial games, and number of players. The methods used in making these classifications are template matching and logical deduction. This master's thesis focuses on solving only subtraction games, since they are easily analysed, while remaining interesting. Presented here is a solution, that can analyse the most common constructs, to solving a slightly generalised form of subtraction games. The classification and solving is tested on a suite of games constructed for this master's project.
    BibTeX: 
    @mastersthesis{Edin2007,
  author = {Erik Edin},
  title = {Classification and Solutions of General Combinatorial Games},
  school = {Royal Institute of Technology, Stockholm, Sweden},
  year = {2007},
  url = {http://www.nada.kth.se/utbildning/grukth/exjobb/rapportlistor/2007/rapporter07/edin_erik_07048.pdf}
}
    Finnsson, H. CADIA-Player: A General Game Playing Agent

    [Abstract] [BibTeX]

    		 2007 School: Reykjavík University - School of Computer Science  mastersthesis URL 
    Abstract: The aim of General Game Playing (GGP) is to create intelligent agents that can automatically learn how to play many different games well without any human intervention, given only a description of the game rules. This forces the agents to be able to learn a strategy without having any domain-specific knowledge provided by their developers. The most successful GGP agents have so far been based on the traditional approach of using game-tree search augmented with an automatically learned evaluation function for encapsulating the domain-specific knowledge. In this thesis we describe CADIAPlayer, a GGP agent that instead uses a simulation-based approach to reason about its actions. More specifically, it uses Monte Carlo rollouts with upper confidence bounds for trees (UCT) as its main search procedure. CADIAPlayer has already proven the effectiveness of this simulation-based approach in the context of GGP by winning the Third Annual GGP Competition. We describe its implementation as well as several algorithmic improvements for making the simulations more effective. Empirical data is presented showing that CADIA-Player outperforms naïve Monte Carlo by close to 90% winning ratio on average on a wide range of games, including Checkers and Othello. We further investigate the relative importance of UCT?s actionselection rule, its memory model, and the various enhancements in achieving this result.
    BibTeX: 
    @mastersthesis{Finnsson2007,
  author = {Hilmar Finnsson},
  title = {CADIA-Player: A General Game Playing Agent},
  school = {Reykjavík University - School of Computer Science},
  year = {2007},
  url = {http://ru.is/lisalib/getfile.aspx?itemid=9558}
}
    Finnsson, H. & Björnsson, Y. Simulation Control in General Game Playing Agents

    [BibTeX]

    		 2009 Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09)  inproceedings  
    BibTeX: 
    @inproceedings{Finnsson2009,
  author = {Hilmar Finnsson and Yngvi Björnsson},
  title = {Simulation Control in General Game Playing Agents},
  booktitle = {Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09)},
  year = {2009}
}
    Finnsson, H. & Björnsson, Y. Simulation-based Approach to General Game Playing

    [Abstract] [BibTeX]

    		 2008 AAAI  inproceedings URL 
    Abstract: The aim of General Game Playing (GGP) is to create intelligent agents that automatically learn how to play many different games at an expert level without any human intervention. The most successful GGP agents in the past have used traditional game-tree search combined with an automatically learned heuristic function for evaluating game states. In this paper we describe a GGP agent that instead uses a Monte Carlo/UCT simulation technique for action selection, an approach recently popularized in computer Go. Our GGP agent has proven its effectiveness by winning last year?s AAAI GGP Competition. Furthermore, we introduce and empirically evaluate a new scheme for automatically learning search-control knowledge for guiding the simulation playouts, showing that it offers significant benefits for a variety of games.
    BibTeX: 
    @inproceedings{Finnsson2008,
  author = {Hilmar Finnsson and Yngvi Björnsson},
  title = {Simulation-based Approach to General Game Playing},
  booktitle = {AAAI},
  publisher = {AAAI Press},
  year = {2008},
  url = {http://ru.is/faculty/hif/papers/cadiaplayer_aaai08.pdf}
}
    Genesereth, M.R., Love, N. & Pell, B. General Game Playing: Overview of the AAAI Competition

    [Abstract] [BibTeX]

    		 2005 AI Magazine
    Vol. 26(2), pp. 62-72     		 article URL 
    Abstract: A General Game Playing System is one that can accept a formal description of a game and play the game effectively without human intervention. Unlike specialized game players, such as Deep Blue, general game players do not rely on algorithms designed in advance for specific games; and, unlike Deep Blue, they are able to play different kinds of games. In order to promote work in this area, the AAAI is sponsoring an open competition at this summer's National Conference. This article is an overview of the technical issues and logistics associated with this summer?s competition, as well as the relevance of General Game Playing to the long range goals of artificial intelligence.
    BibTeX: 
    @article{Genesereth2005,
  author = {Michael R. Genesereth and Nathaniel Love and Barney Pell},
  title = {General Game Playing: Overview of the AAAI Competition},
  journal = {AI Magazine},
  year = {2005},
  volume = {26},
  number = {2},
  pages = {62--72},
  url = {http://games.stanford.edu/competition/misc/aaai.pdf}
}
    Günther, M. Automatic Feature Construction for General Game Playing

    [Abstract] [BibTeX]

    		 2008 School: TU-Dresden  mastersthesis URL 
    Abstract: A central challenge is the automatic construction of an evaluation function that can estimate the winning chances for any position encountered during game-tree search. An evaluation function is a weighted combination of features: numerical functions that identify important aspects of a position. This thesis presents a method to generate a set of features for General Game Playing, based on previous work on knowledge-based feature generation. Moreover, a method is developed that quickly selects features for inclusion in the evaluation function. The feature construction method is combined with TD(lambda) reinforcement learning to produce a complete evaluation function. It is shown that the method could successfully generate an evaluation function for many general games, and an empirical evaluation of its quality is presented.
    BibTeX: 
    @mastersthesis{Guenther2008,
  author = {Martin Günther},
  title = {Automatic Feature Construction for General Game Playing},
  school = {TU-Dresden},
  year = {2008},
  url = {http://www.inf.tu-dresden.de/content/institutes/ki/cl/study/assignments/download/diplom_guenther_feature_construction.pdf}
}
    Günther, M. Decomposition of Single Player Games

    [Abstract] [BibTeX]

    		 2007 School: TU-Dresden  misc URL 
    Abstract: The goal of General Game Playing is to construct an autonomous agent that can effectively play games it has never encountered before. Only the game rules are given explicitly; most useful properties of a game are implicit and need to be deduced. One particular such property is decomposability into separate subgames that have little interaction with each other. This work presents a method to decompose a given single-player game into its subgames by analyzing the preconditions and effects of actions. Moreover, two different search algorithms that exploit this information are proposed. Both are assessed and compared with regard to their optimality and computational complexity.
    BibTeX: 
    @misc{Guenther2007,
  author = {Martin Günther},
  title = {Decomposition of Single Player Games},
  school = {TU-Dresden},
  year = {2007},
  url = {http://www.inf.tu-dresden.de/content/institutes/ki/cl/study/assignments/download/beleg_guenther_subgame_detection.pdf}
}
    Günther, M., Schiffel, S. & Thielscher, M. Factoring General Games

    [Abstract] [BibTeX]

    		 2009 Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09), pp. 27-34  inproceedings URL 
    Abstract: The goal of General Game Playing is to construct an autonomous agent that can play games it has never encountered before. Only the game rules are given explicitly; interesting and useful properties of a game are implicit and need to be deduced. One particular such property is decomposability into separate subgames that have little interaction with each other. We present a method to decompose a given game into its subgames by analyzing the preconditions and effects of actions. Moreover, a search algorithm that exploits this information in single-player games is proposed.
    BibTeX: 
    @inproceedings{MartinGuenther2009,
  author = {Martin Günther and Stephan Schiffel and Michael Thielscher},
  title = {Factoring General Games},
  booktitle = {Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09)},
  year = {2009},
  pages = {27-34},
  url = {http://www.general-game-playing.de/downloads/GIGA09_factoring_general_games.pdf}
}
    Hinrichs, T. Automatically Proving Playability through Abstraction

    [BibTeX]

    		 2006   techreport URL 
    BibTeX: 
    @techreport{Hinrichs2006,
  author = {Timothy Hinrichs},
  title = {Automatically Proving Playability through Abstraction},
  year = {2006},
  url = {http://people.cs.uchicago.edu/~thinrich/papers/playability.pdf}
}
    van der Hoek, W., Ruan, J. & Wooldridge, M. Strategy Logics and the Game Description Language

    [Abstract] [BibTeX]

    		 2007 Proceedings of the Workshop on Logic, Rationality and Interaction  inproceedings URL 
    Abstract: The Game Description Language (GDL) is a special purpose declarative language for defining games. GDL is used in the AAAI General Game Playing Competition, which tests the ability of computer programs to play games in general, rather than just to play a specific game. Participants in the competition are provided with a game specified in GDL, and then required to play this game. Recently, there has been much interest in the use of strategic cooperation logics for reasoning about game-like scenarios - Alternatingtime Temporal Logic (ATL) is perhaps the best known example. The aim of this paper is to make a link between ATL and GDL. We show that a GDL specification can be viewed as a specification of an ATL model, and that ATL can thus be interpreted over GDL specifications. Our main result is that it is possible to translate a propositional GDL specification into an "equivalent" ATL specification, which is only polynomially larger than the original GDL specification. As a corollary, we are able to characterise the complexity of reasoning about GDL-specified games using ATL: it is EXPTIME-complete.
    BibTeX: 
    @inproceedings{Hoek2007,
  author = {W. van der Hoek and J. Ruan and M. Wooldridge},
  title = {Strategy Logics and the Game Description Language},
  booktitle = {Proceedings of the Workshop on Logic, Rationality and Interaction},
  year = {2007},
  url = {http://www.csc.liv.ac.uk/~mjw/pubs/lori2007a.pdf}
}
    Holt, A. General Game Playing Systems

    [Abstract] [BibTeX]

    		 2008 School: Technical University of Denmark  mastersthesis URL 
    Abstract: General Game Playing (GGP) is a field in artificial intelligence (AI) that deals with systems that, provided with only the rules of an arbitrary game, can play the game in an intelligent way. This problem is much harder than making a computer play a specificc game since you cannot rely on predefined evaluation functions or any other domain specific knowledge. It is also more interesting from an AI point of view since the computer needs to show some intelligent behaviour in order to come up with a good move instead of just following a predefined formula. In this report we will investigate different methods of making such a system. We will look at what others have done, and an actual implementation of our own general game player is presented. This implementation can either use the minimax algorithm with a simulation based evaluation mechanism or the UCT algorithm based on Monte Carlo simulations. In the end of this report these two techniques are compared by playing against each other in different games. These comparisons shows that the minimax algorithm with an evaluation function is a good choice in GGP but the UCT algorithm can be very strong when given enough time or computational resources to make a suitable amount of simulations.
    BibTeX: 
    @mastersthesis{Holt2008,
  author = {Andreas Holt},
  title = {General Game Playing Systems},
  school = {Technical University of Denmark},
  year = {2008},
  url = {http://orbit.dtu.dk/getResource?recordId=228660&objectId=1&versionId=1}
}
    Kaiser, D.M. The Design and Implementation of a Successful General Game Playing Agent

    [Abstract] [BibTeX]

    		 2007 FLAIRS Conference, pp. 110-115  inproceedings URL 
    Abstract: General Game Playing is the problem of designing an agent capable of playing any previously unknown game when given only the rules. This paper describes the implementation architecture and design issues behind Ogre, a General Game Playing agent designed at Florida International University. Our main contribution is an innovative algorithm for automatically generating efficient evaluation functions for previously unfamiliar games. The system competed successfully at the second AAAI General Game Playing competition held at AAAI-06.
    BibTeX: 
    @inproceedings{Kaiser2007,
  author = {David M. Kaiser},
  title = {The Design and Implementation of a Successful General Game Playing Agent},
  booktitle = {FLAIRS Conference},
  publisher = {AAAI Press},
  year = {2007},
  pages = {110--115},
  url = {http://faculty.mdc.edu/dkaiser/papers/The%20Design%20and%20Impl3v4.pdf}
}
    Kaiser, D.M. Automatic Feature Extraction for Autonomous General

    Game Playing Agents

    [Abstract] [BibTeX]

    		 2007 Proceedings of the Sixth Intl. Joint Conf. on Autonomous Agents and Multiagent Systems  inproceedings URL 
    Abstract: The General Game Playing (GGP) problem is concerned with developing systems capable of playing many different games, even games the system has never encountered before. Successful GGP agents must be able to extract relevant features from the formal game description and construct effective search heuristics. In this article, we present a procedure by which autonomous General Game Playing agents can generate effective and efficient search heuristics from the formal game description. The major aspect of our approach is an innovative technique to automatically extract critical features from the game structure. Our method has been incorporated into a fully implemented system that came in fourth place at the second General Game Playing Competition held at AAAI-06.
    BibTeX: 
    @inproceedings{Kaiser2007a,
  author = {David M. Kaiser},
  title = {Automatic Feature Extraction for Autonomous General
    Game Playing Agents},
  booktitle = {Proceedings of the Sixth Intl. Joint Conf. on Autonomous Agents and Multiagent Systems},
  year = {2007},
  url = {http://faculty.mdc.edu/dkaiser/papers/Automatic%20Feature%20Extraction%20v2.pdf}
}
    Kaiser, D.M. The structure of games

    [Abstract] [BibTeX]

    		 2007 School: Florida International University  phdthesis URL 
    Abstract: 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.
    BibTeX: 
    @phdthesis{Kaiser2007b,
  author = {David M. Kaiser},
  title = {The structure of games},
  school = {Florida International University},
  year = {2007},
  url = {http://digitalcommons.fiu.edu/cgi/viewcontent.cgi?article=1002&context=etd}
}
    Kirci, M., Schaeffer, J. & Sturtevant, N. Feature Learning Using State Differences

    [Abstract] [BibTeX]

    		 2009 Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09)  inproceedings URL 
    Abstract: The goal of General Game playing (GGP) can be described as designing computer programs that can play a variety of games when given a game description. Learning algorithms have not been an essential part of all successful GGP programs. This paper presents a feature learning approach, GIFL, for 2-player, alternating move games in GGP using state differences. The algorithm is simple, robust and improves the quality of play.
    BibTeX: 
    @inproceedings{Kirci2009,
  author = {Mesut Kirci and Jonathan Schaeffer and Nathan Sturtevant},
  title = {Feature Learning Using State Differences},
  booktitle = {Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09)},
  year = {2009},
  url = {http://www.cs.ualberta.ca/~nathanst/papers/GGPfeatures.pdf}
}
    Kissmann, P. & Edelkamp, S. Instantiating General Games

    [BibTeX]

    		 2009 Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09)  inproceedings  
    BibTeX: 
    @inproceedings{Kissmann2009,
  author = {Peter Kissmann and Stefan Edelkamp},
  title = {Instantiating General Games},
  booktitle = {Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09)},
  year = {2009}
}
    Kostenko, A. Calculating End Game Databases for General Game Playing

    [Abstract] [BibTeX]

    		 2007 School: TU-Dresden  mastersthesis URL 
    Abstract: The issue of end game database construction is known and well-studied in various specific gaming domains like Chess and Checkers. However with the arrival of the General Game Playing (GGP) challenge the automatic construction of end game databases attracted focus again. The current work presents a sound and complete algorithm for automated construction of end game databases within the GGP setting. The theoretical base for the algorithm rests on regression introduced in the Situation Calculus. Along with the theoretical base a sample implementation constructed on top of FLUX (a Fluent Calculus implementation) is presented.
    BibTeX: 
    @mastersthesis{Kostenko2007,
  author = {Arsen Kostenko},
  title = {Calculating End Game Databases for General Game Playing},
  school = {TU-Dresden},
  year = {2007},
  url = {http://www.inf.tu-dresden.de/content/institutes/ki/cl/study/assignments/download/Arsen_Kostenko_MSc.pdf}
}
    Kuhlmann, G., Dresner, K. & Stone, P. Automatic Heuristic Construction in a Complete General Game Player

    [Abstract] [BibTeX]

    		 2006 Proceedings of the Twenty-First National Conference on Artificial Intelligence, pp. 1457-62  inproceedings URL 
    Abstract: Computer game players are typically designed to play a single game: today's best chess-playing programs cannot play checkers, or even tic-tac-toe. General Game Playing is the problem of designing an agent capable of playing many different previously unseen games. The first AAAI General Game Playing Competition was held at AAAI 2005 in order to promote research in this area. In this article, we survey some of the issues involved in creating a general game playing system and introduce our entry to that event. The main feature of our approach is a novel method for automatically constructing effective search heuristics based on the formal game description. Our agent is fully implemented and tested in a range of different games.
    BibTeX: 
    @inproceedings{Kuhlmann2006,
  author = {Gregory Kuhlmann and Kurt Dresner and Peter Stone},
  title = {Automatic Heuristic Construction in a Complete General Game Player},
  booktitle = {Proceedings of the Twenty-First National Conference on Artificial Intelligence},
  year = {2006},
  pages = {1457--62},
  url = {http://www.cs.utexas.edu/~pstone/Papers/bib2html-links/AAAI06-ggp.pdf}
}
    Kuhlmann, G. & Stone, P. Graph-Based Domain Mapping for Transfer Learning in General Games

    [Abstract] [BibTeX]

    		 2007 Proceedings of The Eighteenth European Conference on Machine Learning  inproceedings URL 
    Abstract: A general game player is an agent capable of taking as input a description of a game's rules in a formal language and proceeding to play without any subsequent human input. To do well, an agent should learn from experience with past games and transfer the learned knowledge to new problems. We introduce a graph-based method for identifying previously encountered games and prove its robustness formally. We then describe how the same basic approach can be used to identify similar but non-identical games. We apply this technique to automate domain mapping for value function transfer and speed up reinforcement learning on variants of previously played games. Our approach is fully implemented with empirical results in the general game playing system.
    BibTeX: 
    @inproceedings{Kuhlmann2007,
  author = {Gregory Kuhlmann and Peter Stone},
  title = {Graph-Based Domain Mapping for Transfer Learning in General Games},
  booktitle = {Proceedings of The Eighteenth European Conference on Machine Learning},
  year = {2007},
  url = {http://www.cs.utexas.edu/~pstone/Papers/bib2html-links/ECML07-rulegraphs.pdf}
}
    Kulick, J., Block, M. & Rojas, R. General Game Playing mit stochastischen Spielen

    [Abstract] [BibTeX]

    		 2009   techreport URL 
    Abstract: In der Game Description Language lassen sich rundenbasierte Spiele mit vollständiger Information ohne stochastische Ereignisse beschreiben. Obwohl die Sprache erst 2005 entwickelt wurde, hat sie eine hohe Akzeptanz innerhalb der Wissenschaftgemeinde. Es werden Programme entwickelt, die aus den Spielbeschreibungen Strategien ableiten und die Spiele erfolgreich spielen. In dieser Arbeit wird eine Erweiterung der Sprache GDL vorgestellt, mit der nun auch Spiele mit stochastischen Elementen beschrieben und gespielt werden können.
    BibTeX: 
    @techreport{Kulick2009,
  author = {Johannes Kulick and Marco Block and Raul Rojas},
  title = {General Game Playing mit stochastischen Spielen},
  year = {2009},
  url = {http://www.inf.fu-berlin.de/publications/techreports/tr2009/B-09-08/B-09-08.pdf}
}
    Levinson, R.A. General Game-Playing and Reinforcement Learning

    [Abstract] [BibTeX]

    		 1996 Computational Intelligence
    Vol. 12(1), pp. 155-176     		 article URL 
    Abstract: This paper gives a blueprint for the development of a fully domain-independent single-agent and multi-agent heuristic search system. It gives a graph-theoretic representation of search problems based on conceptual graphs, and outlines two different learning systems. One, an "informed learner," makes use of the the graph-theoretic definition of a search problem or game in playing and adapting to a game in the given environment. The other, a "blind learner," is not given access to the rules of a domain, but must discover and then exploit the underlying mathematical structure of a given domain. Relevant work of others is referenced within the context of the blueprint. To illustrate further how one might go about creating general game-playing agents, we show how we can generalize the understanding obtained with the Morph chess system to all games involving the interactions of abstract mathematical relations. An example of a monitor for such domains is presented, along with an implementation of a blind and informed learning system known as MorphII. Performance results with MorphII are preliminary but encouraging and provide a few more data points with which to understand and evaluate the blueprint.
    BibTeX: 
    @article{Levinson1996,
  author = {Robert A. Levinson},
  title = {General Game-Playing and Reinforcement Learning},
  journal = {Computational Intelligence},
  year = {1996},
  volume = {12},
  number = {1},
  pages = {155--176},
  note = {Special Issue on Games: Structure and Learning},
  url = {ftp://ftp.cse.ucsc.edu/pub/tr/ucsc-crl-95-06.ps.Z}
}
    Love, N., Hinrichs, T., Haley, D., Schkufza, E. & Genesereth, M. General Game Playing: Game Description Language Specification

    [BibTeX]

    		 2008   techreport URL 
    BibTeX: 
    @techreport{Love2008,
  author = {Nathaniel Love and Timothy Hinrichs and David Haley and Eric Schkufza and Michael Genesereth},
  title = {General Game Playing: Game Description Language Specification},
  year = {2008},
  note = {most recent version should be available at http://games.stanford.edu/},
  url = {http://games.stanford.edu/language/spec/gdl_spec_2008_03.pdf}
}
    Michulke, D. & Thielscher, M. Neural Networks for State Evaluation in General Game Playing

    [Abstract] [BibTeX]

    		 2009 Proceedings of the European Conference on Machine Learning (EMCL)  inproceedings URL 
    Abstract: Unlike traditional game playing, General Game Playing is concerned with agents capable of playing classes of games. Given the rules of an unknown game, the agent is supposed to play well without human intervention. For this purpose, agent systems that use deterministic game tree search need to automatically construct a state value function to guide search. Successful systems of this type use evaluation functions derived solely from the game rules, thus neglecting further improvements by experience. In addition, these functions are fixed in their form and do not necessarily capture the game's real state value function. In this work we present an approach for obtaining evaluation functions on the basis of neural networks that overcomes the aforementioned problems. A network initialization extracted from the game rules ensures reasonable behavior without the need for prior training. Later training, however, can lead to significant improvements in evaluation quality, as our results indicate.
    BibTeX: 
    @inproceedings{Michulke2009,
  author = {Daniel Michulke and Michael Thielscher},
  title = {Neural Networks for State Evaluation in General Game Playing},
  booktitle = {Proceedings of the European Conference on Machine Learning (EMCL)},
  year = {2009},
  url = {http://www1.inf.tu-dresden.de/~mit/publications/conferences/ECML09.pdf}
}
    Pell, B. A Strategic Metagame Player for General Chess-Like Games

    [Abstract] [BibTeX]

    		 1996 Computational Intelligence
    Vol. 12, pp. 177-198     		 article URL 
    Abstract: This paper introduces METAGAMER, the first program designed within the paradigm of Meta-Game Playing. This programs plays Metagame in the class of symmetric chess-like games, which includes chess, Chinese-chess, checkers, draughts, and Shogi. METAGAMER takes as input the rules of a specific game and analyses those rules to construct for that game an efficient representation and an evaluation function, for use by a generic search engine. The strategic analysis performed by METAGAMER relates a set of general knowledge sources to the details of the particular game. Amonge other properties, this analysis determines the relative value of the different pieces in a given game. Although METAGAMER does not learn from experience, the values resulting from its analysis are qualitatively similar to values used by experts on known games, and are sufficiont to produce competitive perfomance the first time METAGAMER actually plays each new game. Besides being the first Metagame-playing program, this is the first program to have derived useful piece values directly from analysis of the rules of different games. This paper describes the knowledge implemented in METAGAMER, illustrates the piece values METAGAMER derives for chess and checkers, and discusses experiments with METAGAMER on both existing and newly generated games.
    BibTeX: 
    @article{Pell1996,
  author = {Barney Pell},
  title = {A Strategic Metagame Player for General Chess-Like Games},
  journal = {Computational Intelligence},
  year = {1996},
  volume = {12},
  pages = {177--198},
  url = {http://www.barneypell.com/papers/metagamer-cij.pdf}
}
    Pell, B. Strategy generation and evaluation for meta-game playing

    [Abstract] [BibTeX]

    		 1993 School: University of Cambridge  phdthesis URL 
    Abstract: Meta-Game Playing (Metagame) is a new paradigm for research in game-playing in which we design programs to take in the rules of unknown games and play those games without human assistance. Strong performance in this new paradigm is evidence that the program, instead of its human designer has performed the analysis of each specific game. SCL- Metagame is a concrete Metagame research problem based around the class of symmetric chess-like games. The class includes the games of chess, draughts, noughts and crosses, Chinese-chess, and Shogi. An implemented game generator produces new games in this class, some of which are objects of interest in their own right.; METAGAMER is a program that plays SCL-Metagame. The program takes as input the rules of a specific game and analyses those rules to construct for that game an efficient representation and an evaluation function, both for use with a generic search engine. The strategic analysis performed by the program relates a set of general knowledge sources to the details of the particular game. Among other properties, this analysis determines the relative value of the different pieces in a given game. Although METAGAMER does not learn from experience, the values resulting from its analysis are qualitatively similar to values used by experts on known games, and are sufficient to produce competitive performance the first time the program actually plays each game it is given.; This appears to be the first program to have derived useful piece values directly from analysis of the rules of different games. Experiments show that the knowledge implemented in METAGAMER is useful on games unknown to its programmer in advance of the competition and make it seem likely that future programs which incorporate learning and more sophisticated active-analysis techniques will have a demonstrable competitive advantage on this new problem. When playing the known games of chess and checkers against humans and specialised programs, METAGAMER has derived from more general principles some strategies which are familiar to players of those games and which are hard-wired in many game-specific programs.
    BibTeX: 
    @phdthesis{Pell1993,
  author = {Barney Pell},
  title = {Strategy generation and evaluation for meta-game playing},
  publisher = {Cambridge [Cambridgeshire] :University of Cambridge, Computer Laboratory,},
  school = {University of Cambridge},
  year = {1993},
  url = {http://www.barneypell.com/papers/pell-thesis.pdf}
}
    Pell, B. Logic programming for general game-playing

    [Abstract] [BibTeX]

    		 1993 (UCAM-CL-TR-302)  techreport URL 
    Abstract: Meta-Game Playing is a new approach to games in Artificial Intelligence, where we construct programs to play new games in a well-defined class, which are output by an automatic game generator. As the specific games to be played are not known in advance, a degree of human bias is eliminated, and playing programs are required to perform any game-specific optimisations without human assistance. The attempt to construct a general game-playing program is made difficult by the opposing goals of generality and efficiency. This paper shows how application of standard techniques in logic-programming (abstract interpretation and partial evaluation) makes it possible to achieve both of these goals. Using these techniques, we can represent the semantics of a large class of games in a general and declarative way, but then have the program transform this representation into a more efficient version once it is presented with the rules of a new game. This process can be viewed as moving some of the responsibility for game analysis (that concerned with efficiency) from the researcher to the program itself.
    BibTeX: 
    @techreport{Pell1993a,
  author = {Barney Pell},
  title = {Logic programming for general game-playing},
  year = {1993},
  number = {UCAM-CL-TR-302},
  url = {http://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-302.ps.gz}
}
    Quenault, M. & Cazenave, T. Extended General Gaming Model

    [Abstract] [BibTeX]

    		 2007 Computer Games Workshop, pp. 195-204  inproceedings URL 
    Abstract: General Gaming is a field of research on systems that can manage various game descriptions and play them effectively. These systems, unlike specialized ones, must deal with various kinds of games and cannot rely on any kind of game algorithms designed upstream, such as DEEPER BLUE ones. This field is currently mainly restricted to complete-information board games. Given the lack of more various games in the field of General Gaming, we propose in this article a very open functional model and its tested implementation. This model is able to bring together on the same engine both card games and board games, both complete- and incomplete-information games, both deterministic and chance games.
    BibTeX: 
    @inproceedings{Quenault2007,
  author = {M. Quenault and T. Cazenave},
  title = {Extended General Gaming Model},
  booktitle = {Computer Games Workshop},
  year = {2007},
  pages = {195-204},
  url = {http://www.ai.univ-paris8.fr/~cazenave/extendedggmodel.pdf}
}
    Reisinger, J., Bahceci, E., Karpov, I. & Miikkulainen, R. Coevolving Strategies for General Game Playing

    [Abstract] [BibTeX]

    		 2007 IEEE Symposium on Computational Intelligence and Games, pp. 320-327  inproceedings URL 
    Abstract: The General Game Playing Competition poses a unique challenge for Artificial Intelligence. To be successful, a player must learn to play well in a limited number of example games encoded in first-order logic and then generalize its game play to previously unseen games with entirely different rules. Because good opponents are usually not available, learning algorithms must come up with plausible opponent strategies in order to benchmark performance. One approach to simultaneously learning all player strategies is coevolution. This paper presents a coevolutionary approach using NeuroEvolution of Augmenting Topologies to evolve populations of game state evaluators. This approach is tested on a sample of games from the General Game Playing Competition and shown to be effective: It allows the algorithm designer to minimize the amount of domain knowledge built into the system, which leads to more general game play and allows modeling opponent strategies efficiently. Furthermore, the General Game Playing domain proves to be a powerful tool for developing and testing coevolutionary methods.
    BibTeX: 
    @inproceedings{Reisinger2007,
  author = {Joseph Reisinger and Erkin Bahceci and Igor Karpov and Risto Miikkulainen},
  title = {Coevolving Strategies for General Game Playing},
  booktitle = {IEEE Symposium on Computational Intelligence and Games},
  year = {2007},
  pages = {320-327},
  url = {http://ieeexplore.ieee.org/iel5/4219012/4219013/04219060.pdf?isnumber=4219013&prod=CNF&arnumber=4219060&arSt=320&ared=327&arAuthor=Reisinger%2C+J.%3B+Bahceci%2C+E.%3B+Karpov%2C+I.%3B+Miikkulainen%2C+R.}
}
    Richards, M. & Amir, E. Information Set Sampling for General Imperfect Information Positional Games

    [BibTeX]

    		 2009 Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09)  inproceedings  
    BibTeX: 
    @inproceedings{Richards2009,
  author = {Mark Richards and Eyal Amir},
  title = {Information Set Sampling for General Imperfect Information Positional Games},
  booktitle = {Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09)},
  year = {2009}
}
    Ruan, J., van der Hoek, W. & Wooldridge, M. Verification of Games in the Game Description Language

    [Abstract] [BibTeX]

    		 2009 Journal of Logic and Computation(4)  article DOI URL 
    Abstract: The Game Description Language (GDL) is a special purpose declarative language for defining games. GDL is used in the AAAI General Game Playing Competition, which tests the ability of computer programs to play games in general, rather than just the ability to play a specific game. Participants in the competition are provided with a previously unknown game specified in GDL, and are required to dynamically and autonomously determine how best to play this game. Recently, there has been much interest in the use of strategic cooperation logics for reasoning about game-like scenarios—the Alternating-time Temporal Logic (ATL) of Alur, Henzinger, and Kupferman is perhaps the best known example. Such logics are specifically intended to support reasoning about game-theoretic properties of multi-agent systems. In short, the aim of this article is to make a concrete link between ATL and GDL, with the ultimate goal of using ATL to reason about GDL-specified games. We make the following contributions. First, we demonstrate that GDL can be understood as a specification language for ATL models, and prove that the problem of interpreting ATL formulae over propositional GDL descriptions is EXPTIME-complete. Second, we use ATL to characterize a class of ‘fair playability’ conditions, which might or might not hold of various games.
    BibTeX: 
    @article{Ruan2009,
  author = {Ji Ruan and Wiebe van der Hoek and Michael Wooldridge},
  title = {Verification of Games in the Game Description Language},
  journal = {Journal of Logic and Computation},
  year = {2009},
  number = {4},
  url = {http://www.csc.liv.ac.uk/~mjw/pubs/jlc2009a.pdf},
  doi = {http://dx.doi.org/10.1093/logcom/exp039}
}
    Schiffel, S. Symmetry Detection in General Game Playing

    [Abstract] [BibTeX]

    		 2009 Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09), pp. 67-74  inproceedings URL 
    Abstract: We develop a method for detecting symmetries in arbitrary games and exploiting these symmetries when using tree search to play the game. Games in the General Game Playing domain are given as a set of logic based rules defining legal moves, their effects and goals of the players. The presented method transforms the rules of a game into a vertex-labeled graph such that automorphisms of the graph correspond with symmetries of the game. The algorithm detects many kinds of symmetries that often occur in games, e.g., rotation and reflection symmetries of boards, interchangeable objects, and symmetric roles. A transposition table is used to efficiently exploit the symmetries in many games.
    BibTeX: 
    @inproceedings{Schiffel2009b,
  author = {Stephan Schiffel},
  title = {Symmetry Detection in General Game Playing},
  booktitle = {Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09)},
  year = {2009},
  pages = {67-74},
  url = {http://www.general-game-playing.de/downloads/GIGA09_symmetry_detection.pdf}
}
    Schiffel, S. & Thielscher, M. A Multiagent Semantics for the Game Description Language

    [Abstract] [BibTeX]

    		 2009 International Conference on Agents and Artificial Intelligence (ICAART)  inproceedings URL 
    Abstract: The Game Description Language (GDL) has been developed for the purpose of formalizing game rules. It serves as the input language for general game players, which are systems that learn to play previously unknown games without human intervention. In this paper, we show how GDL descriptions can be intepreted as multiagent domains and, conversely, how a large class of multiagent environments can be specified in GDL. The resulting specifications are declarative, compact, and easy to understand and maintain. At the same time they can be fully automatically understood and used by autonomous agents who intend to participate in these environments. Our main result is a formal characterization of the class of multiagent domains that serve as formal semantics for—and can be described in—the Game Description Language.
    BibTeX: 
    @inproceedings{Schiffel2009a,
  author = {Stephan Schiffel and Michael Thielscher},
  title = {A Multiagent Semantics for the Game Description Language},
  booktitle = {International Conference on Agents and Artificial Intelligence (ICAART)},
  publisher = {Springer},
  year = {2009},
  url = {http://www.general-game-playing.de/downloads/SPRINGER09_Schiffel_Thielscher.pdf}
}
    Schiffel, S. & Thielscher, M. Automated Theorem Proving for General Game Playing

    [Abstract] [BibTeX]

    		 2009 Proceedings of IJCAI'09  inproceedings URL 
    Abstract: A general game player is a system that understands the rules of an unknown game and learns to play this game well without human intervention. To succeed in this endeavor, systems need to be able to extract and prove game-specific knowledge from the mere game rules. We present a practical approach to this challenge with the help of Answer Set Programming. The key idea is to reduce the automated theorem proving task to a simple proof of an induction step and its base case. We prove correctness of this method and report on experiments with an off-the-shelf Answer Set Programming system in combination with a successful general game player.
    BibTeX: 
    @inproceedings{Schiffel2009c,
  author = {Stephan Schiffel and Michael Thielscher},
  title = {Automated Theorem Proving for General Game Playing},
  booktitle = {Proceedings of IJCAI'09},
  year = {2009},
  url = {http://www1.inf.tu-dresden.de/~mit/publications/conferences/IJCAI09.pdf}
}
    Schiffel, S. & Thielscher, M. Automatic construction of a heuristic search function for General Game Playing

    [Abstract] [BibTeX]

    		 2007 Proceedings of the Seventh IJCAI International Workshop on Nonmontonic Reasoning, Action and Change  inproceedings URL 
    Abstract: General Game Playing (GGP) is the art of designing programs that are capable of playing previously unknown games of a wide variety by being told nothing but the rules of the game. This is in contrast to traditional computer game players like Deep Blue, which are designed for a particular game and can't adapt automatically to modifications of the rules, let alone play completely different games. General Game Playing is intended to foster the development of integrated cognitive information processing technology. In this article we present an approach to General Game Playing using a novel way of automatically constructing a search heuristics from a formal game description. Our system is being tested with a wide range of different games. Most notably, it is the winner of the AAAI GGP Competition 2006.
    BibTeX: 
    @inproceedings{Schiffel2007,
  author = {Stephan Schiffel and Michael Thielscher},
  title = {Automatic construction of a heuristic search function for General Game Playing},
  booktitle = {Proceedings of the Seventh IJCAI International Workshop on Nonmontonic Reasoning, Action and Change},
  year = {2007},
  url = {http://www.fluxagent.org/download.php?file=07-SchiffelThielscher-IJCAI.pdf}
}
    Schiffel, S. & Thielscher, M. Fluxplayer: A Successful General Game Player

    [Abstract] [BibTeX]

    		 2007 Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI-07), pp. 1191-1196  inproceedings URL 
    Abstract: General Game Playing (GGP) is the art of designing programs that are capable of playing previously unknown games of a wide variety by being told nothing but the rules of the game. This is in contrast to traditional computer game players like Deep Blue, which are designed for a particular game and can't adapt automatically to modifications of the rules, let alone play completely different games. General Game Playing is intended to foster the development of integrated cognitive information processing technology. In this article we present an approach to General Game Playing using a novel way of automatically constructing a position evaluation function from a formal game description. Our system is being tested with a wide range of different games. Most notably, it is the winner of the AAAI GGP Competition 2006.
    BibTeX: 
    @inproceedings{Schiffel2007a,
  author = {Stephan Schiffel and Michael Thielscher},
  title = {Fluxplayer: A Successful General Game Player},
  booktitle = {Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI-07)},
  publisher = {AAAI Press},
  year = {2007},
  pages = {1191--1196},
  url = {http://www.fluxagent.org/download.php?file=07-SchiffelThielscher-AAAI.pdf}
}
    Schkufza, E. Decomposition of Games for Efficient Reasoning

    [Abstract] [BibTeX]

    		 2007 Abstraction, Reformulation, and Approximation, pp. 409-410  inproceedings DOI URL 
    Abstract: General Game Playing (GGP) is an area of research in artificial intelligence that focuses on the representation of games in terms of an abstract language known as Game Description Language (GDL). The abstract nature of that language allows for the development of intelligent agents that without modification can perform competently on games that they have never seen before based on known properties of GDL's uniform and compact syntax. Although GDL is an effective tool for communication, it is less useful as a representational framework for reasoning about games. In its place, the Stanford Logic Group has developed a new class of behavioral models, called Propositional Nets (PNs), with which an algorithm has been designed for determining whether games can be decomposed into independent sub-systems that can be reasoned about independently of one another.
    BibTeX: 
    @inproceedings{Schkufza2007,
  author = {Eric Schkufza},
  title = {Decomposition of Games for Efficient Reasoning},
  booktitle = {Abstraction, Reformulation, and Approximation},
  publisher = {Springer Berlin / Heidelberg},
  year = {2007},
  pages = {409-410},
  url = {http://www.springerlink.com/content/750hn867578890k3/},
  doi = {http://dx.doi.org/10.1007/978-3-540-73580-9}
}
    Schkufza, E., Love, N. & Genesereth, M.R. Propositional Automata and Cell Automata: Representational Frameworks for Discrete Dynamic Systems

    [Abstract] [BibTeX]

    		 2008
    Vol. 5360Australasian Conference on Artificial Intelligence, pp. 56-66     		 inproceedings URL 
    Abstract: This paper describes and compares two simple, powerful models for formalizing the behavior of discrete dynamic systems: Propositional and Cell Automata. Propositional Automata encode state in terms of boolean propositions, and behavior in terms of boolean gates and latches. Cell Automata generalize the propositional model by encoding state in terms of multi-valued cells, and behavior in terms of comparators and selectors that respond to cell values. While the models are equally expressive, Cell Automata are computationally more efficient than Propositional Automata. Additionally, arbitrary Propositional Automata can be converted to optimal Cell Automata with identical behavioral properties, and Cell Automata can be encoded as a Propositional Automata with only logarithmic increase in size.
    BibTeX: 
    @inproceedings{DBLP:conf/ausai/SchkufzaLG08,
  author = {Eric Schkufza and Nathaniel Love and Michael R. Genesereth},
  title = {Propositional Automata and Cell Automata: Representational Frameworks for Discrete Dynamic Systems},
  booktitle = {Australasian Conference on Artificial Intelligence},
  publisher = {Springer},
  year = {2008},
  volume = {5360},
  pages = {56-66},
  url = {http://dx.doi.org/10.1007/978-3-540-89378-3_6}
}
    Shafiei, M., Sturtevant, N. & Schaeffer, J. Comparing UCT versus CFR in Simultaneous Games

    [Abstract] [BibTeX]

    		 2009 Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09)  inproceedings URL 
    Abstract: Simultaneous move games where all the player have to take their actions simultaneously are a class of games in general game playing. In this paper we analyze how UCT performs in this class of games. We argue that UCT does not converge to a Nash equilibrium in general and the situation that it converges to can be exploited. We also analyze CFR (CounterFactual Regret) and show how it can be used to exploit UCT.
    BibTeX: 
    @inproceedings{Shafiei2009,
  author = {Mohammad Shafiei and Nathan Sturtevant and Jonathan Schaeffer},
  title = {Comparing UCT versus CFR in Simultaneous Games},
  booktitle = {Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09)},
  year = {2009},
  url = {http://www.cs.ualberta.ca/~nathanst/papers/uctcfr.pdf}
}
    Sharma, S. & Kobti, Z. A Multi-Agent Architecture for General Game Playing

    [Abstract] [BibTeX]

    		 2007 Proceedings of the IEEE Symposium on Computational Intelligence and Games  inproceedings URL 
    Abstract: General Game playing, a relatively new field in game research, presents new frontiers in building intelligent game players. The traditional premise for building a good artificially intelligent player is that the game is known to the player and pre-programmed to play accordingly. General game players challenge game programmers by not identifying the game until the beginning of game play. In this paper we explore a new approach to intelligent general game playing employing a self-organizing, multiple-agent evolutionary learning strategy. In order to decide on an intelligent move, specialized agents interact with each other and evolve competitive solutions to decide on the best move, sharing the learnt experience and using it to train themselves in a social environment. In an experimental setup using a simple board game, the evolutionary agents employing a learning strategy by training themselves from their own experiences, and without prior knowledge of the game, demonstrate to be as effective as other strong dedicated heuristics. This approach provides a potential for new intelligent game playing program design in the absence of prior knowledge of the game at hand.
    BibTeX: 
    @inproceedings{Sharma2007,
  author = {Shiven Sharma and Ziad Kobti},
  title = {A Multi-Agent Architecture for General Game Playing},
  booktitle = {Proceedings of the IEEE Symposium on Computational Intelligence and Games},
  year = {2007},
  url = {http://cs.uwindsor.ca/~sharmaw/cig07.pdf}
}
    Sharma, S., Kobti, Z. & Goodwin, S. Coevolving Intelligent Game Players in a Cultural Framework

    [Abstract] [BibTeX]

    		 2009 Proceedings of the IEEE Congress on Evolutionary Computing, Special Session on Computational Intelligence in Games  inproceedings URL 
    Abstract: Game playing has always provided an exciting avenue of research in Artificial Intelligence. Various methodologies and techniques have been developed to build intelligent game players. Coevolution has proven to be successful in learning how to play games with no prior game knowledge. In this paper we develop a coevolutionary system for the General Game Playing framework, where absolutely nothing is known about the game beforehand, and enhance it using Cultural Algorithms. In order to test the effectiveness of complementing coevolution with cultural algorithms, we play matches in several games between our player, a random player and one trained using standard coevolution.
    BibTeX: 
    @inproceedings{Sharma2009,
  author = {Shiven Sharma and Ziad Kobti and Scott Goodwin},
  title = {Coevolving Intelligent Game Players in a Cultural Framework},
  booktitle = {Proceedings of the IEEE Congress on Evolutionary Computing, Special Session on Computational Intelligence in Games},
  year = {2009},
  url = {http://cs.uwindsor.ca/~sharmaw/SKG_CEC.pdf}
}
    Sharma, S., Kobti, Z. & Goodwin, S. Learning and Knowledge Generation in General Games

    [Abstract] [BibTeX]

    		 2008 Proceedings of the IEEE Symposium on Computational Intelligence and Games  inproceedings URL 
    Abstract: General Game Playing (GGP) aims at developing game playing agents that are able to play a variety of games and in the absence of game specific knowledge, become proficient players. Most GGP players have used standard tree-search techniques enhanced by automatic heuristic learning. In this paper we explore knowledge representation and learning in GGP using Reinforcement Learning and Ant Colony Algorithms. Knowledge is created by simulating random games. We test the quality of the knowledge by comparing the performance of players using the knowledge in a variety of games. The ideas presented in this paper provide the potential for a framework for learning and knowledge representation, given the total absence of any prior knowledge.
    BibTeX: 
    @inproceedings{Sharma2008,
  author = {Shiven Sharma and Ziad Kobti and Scott Goodwin},
  title = {Learning and Knowledge Generation in General Games},
  booktitle = {Proceedings of the IEEE Symposium on Computational Intelligence and Games},
  year = {2008},
  url = {http://cs.uwindsor.ca/~sharmaw/cig08.pdf}
}
    Sharma, S., Kobti, Z. & Goodwin, S.D. General Game Playing with Ants

    [Abstract] [BibTeX]

    		 2008
    Vol. 5361Proceedings of the Seventh International Conference on Simulated Evolution And Learning (SEAL'08), pp. 381-390     		 inproceedings URL 
    Abstract: General Game Playing (GGP) aims at developing game playing agents that are able to play a variety of games and, in the absence of pre-programmed game specific knowledge, become proficient players. The challenge of making such a player has led to various techniques being used to tackle the problem of game specific knowledge absence. Most GGP players have used standard tree-search techniques enhanced by automatic heuristic learning, neuroevolution and UCT (Upper Confidence bounds applied to Trees) search, which is a simulation-based tree search. In this paper, we explore a new approach to GGP. We use an Ant Colony System (ACS) to explore the game space and evolve strategies for game playing. Each ant in the ACS is a player with an assigned role, and forages through the game's state space, searching for promising paths to victory. Preliminary results show this approach to be promising. In order to test the architecture, we create matches between players using the knowledge learnt by the ACS and random players.
    BibTeX: 
    @inproceedings{conf/seal/SharmaKG08,
  author = {Shiven Sharma and Ziad Kobti and Scott D. Goodwin},
  title = {General Game Playing with Ants},
  booktitle = {Proceedings of the Seventh International Conference on Simulated Evolution And Learning (SEAL'08)},
  publisher = {Springer},
  year = {2008},
  volume = {5361},
  pages = {381-390},
  url = {http://dx.doi.org/10.1007/978-3-540-89694-4_39}
}
    Sharma, S., Kobti, Z. & Goodwin, S.D. Knowledge Generation for Improving Simulations in UCT for General Game Playing

    [Abstract] [BibTeX]

    		 2008
    Vol. 5360Australasian Conference on Artificial Intelligence     		 inproceedings URL 
    Abstract: General Game Playing (GGP) aims at developing game playing agents that are able to play a variety of games and, in the absence of pre-programmed game specific knowledge, become proficient players. Most GGP players have used standard tree-search techniques enhanced by automatic heuristic learning. The UCT algorithm, a simulation-based tree search, is a new approach and has been used successfully in GGP. However, it relies heavily on random simulations to assign values to unvisited nodes and selecting nodes for descending down a tree. This can lead to slower convergence times in UCT. In this paper, we discuss the generation and evolution of domain-independent knowledge using both state and move patterns. This is then used to guide the simulations in UCT. In order to test the improvements, we create matches between a player using standard the UCT algorithm and one using UCT enhanced with knowledge.
    BibTeX: 
    @inproceedings{DBLP:conf/ausai/SharmaKG08,
  author = {Shiven Sharma and Ziad Kobti and Scott D. Goodwin},
  title = {Knowledge Generation for Improving Simulations in UCT for General Game Playing},
  booktitle = {Australasian Conference on Artificial Intelligence},
  publisher = {Springer},
  year = {2008},
  volume = {5360},
  url = {http://dx.doi.org/10.1007/978-3-540-89378-3_5}
}
    Taylor, M.E., Kuhlmann, G. & Stone, P. Accelerating Search with Transferred Heuristics

    [Abstract] [BibTeX]

    		 2007 ICAPS-07 workshop on AI Planning and Learning  inproceedings URL 
    Abstract: A common goal for transfer learning research is to show that a learner can solve a source task and then leverage the learned knowledge to solve a target task faster than if it had learned the target task directly. A more difficult goal is to reduce the total training time so that learning the source task and target task is faster than learning only the target task. This paper addresses the second goal by proposing a transfer hierarchy for 2-player games. Such a hierarchy orders games in terms of relative solution difficulty and can be used to select source tasks that are faster to learn than a given target task. We empirically test transfer between two types of tasks in the General Game Playing domain, the testbed for an international competition developed at Stanford. Our results show that transferring learned search heuristics from tasks in different parts of the hierarchy can significantly speed up search even when the source and target tasks differ along a number of important dimensions.
    BibTeX: 
    @inproceedings{Taylor2007,
  author = {Matthew E. Taylor and Gregory Kuhlmann and Peter Stone},
  title = {Accelerating Search with Transferred Heuristics},
  booktitle = {ICAPS-07 workshop on AI Planning and Learning},
  year = {2007},
  url = {http://www.cs.utexas.edu/~pstone/Papers/bib2html-links/ICAPS07WS-taylor.pdf}
}
    Thielscher, M. Answer Set Programming for Single-Player Games in General Game Playing

    [Abstract] [BibTeX]

    		 2009 Proceedings of the International Conference on Logic Programming (ICLP)  inproceedings URL 
    Abstract: As a novel, grand AI challenges, General Game Playing is concerned with the development of systems that understand the rules of unknown games and play these games well without human intervention. In this paper, we show how Answer Set Programming can assist a general game player with the special class of single-player games. To this end, we present a translation from the general Game Description Language (GDL) into answer set programs (ASP). Correctness of this mapping is established by proving that the stable models of the resulting ASP coincide with the possible developments of the original GDL game. We report on experiments with single-player games from past AAAI General Game Playing Competitions which substantiate the claim that Answer Set Programming can provide valuable support to general game playing systems for this type of games.
    BibTeX: 
    @inproceedings{Thielscher2009,
  author = {Michael Thielscher},
  title = {Answer Set Programming for Single-Player Games in General Game Playing},
  booktitle = {Proceedings of the International Conference on Logic Programming (ICLP)},
  publisher = {Springer},
  year = {2009},
  url = {http://www1.inf.tu-dresden.de/~mit/publications/conferences/ICLP09.pdf}
}
    Thielscher, M. & Zhang, D. From GDL to a Market Specification Language for General Trading Agents

    [BibTeX]

    		 2009 Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09)  inproceedings  
    BibTeX: 
    @inproceedings{Thielscher2009a,
  author = {Michael Thielscher and Dongmo Zhang},
  title = {From GDL to a Market Specification Language for General Trading Agents},
  booktitle = {Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09)},
  year = {2009}
}
    Utgoff, P.E. Feature Construction for Game Playing

    [Abstract] [BibTeX]

    		 2001 Machines that learn to play games, pp. 131-152  inproceedings URL 
    Abstract: To build an evaluation function for game-playing, one needs to construct informative features that enable accurate relative assessment of a game state. This chapter describes the feature construction problem, and suggests directions for dealing with shortcomings in the present state of the art.
    BibTeX: 
    @inproceedings{Utgoff2001,
  author = {Paul E. Utgoff},
  title = {Feature Construction for Game Playing},
  booktitle = {Machines that learn to play games},
  publisher = {Nova Science Publishers},
  year = {2001},
  pages = {131-152},
  url = {http://ml-www.cs.umass.edu/~utgoff/papers/nova-kubat.pdf}
}
    Waugh, K. Faster State Manipulation in General Games using Generated Code

    [BibTeX]

    		 2009 Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09)  inproceedings  
    BibTeX: 
    @inproceedings{Waugh2009,
  author = {Kevin Waugh},
  title = {Faster State Manipulation in General Games using Generated Code},
  booktitle = {Proceedings of the IJCAI-09 Workshop on General Game Playing (GIGA'09)},
  year = {2009}
}
    Zhao, D. Decomposition of Multi-Player Games

    [Abstract] [BibTeX]

    		 2009 School: TU-Dresden  mastersthesis URL 
    Abstract: Research in General Game Playing aims at building a system which can play arbitrary games. Moreover, the system should not only be able to play any game, but also to play the game fast. Time constraints for choosing moves are a main challenge to the system, especially with very large games. However, some large games can be split into many independent small games (called subgames), and the game can be solved by solving its subgames. This is called decomposition search. We will show in our work that the time complexity of decomposition search for decomposable games is exponentially smaller than that of normal game search.
    BibTeX: 
    @mastersthesis{Zhao2009,
  author = {Dengji Zhao},
  title = {Decomposition of Multi-Player Games},
  school = {TU-Dresden},
  year = {2009},
  url = {http://www.inf.tu-dresden.de/content/institutes/ki/cl/study/assignments/download/dengji_zhao_master_thesis.pdf}
}

    Created by JabRef on 27/08/2009.