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IEEE Transactions on Computational Intelligence and AI in Games

Issue 1 • Date March 2010

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Displaying Results 1 - 10 of 10
  • Table of contents

    Publication Year: 2010, Page(s): C1
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  • IEEE Transactions on Computational Intelligence and AI in Games publication information

    Publication Year: 2010, Page(s): C2
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  • Evolutionary Game Design

    Publication Year: 2010, Page(s):1 - 16
    Cited by:  Papers (16)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1190 KB) | HTML iconHTML

    It is easy to create new combinatorial games but more difficult to predict those that will interest human players. We examine the concept of game quality, its automated measurement through self-play simulations, and its use in the evolutionary search for new high-quality games. A general game system called Ludi is described and experiments conducted to test its ability to synthesize and evaluate n... View full abstract»

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  • RL-DOT: A Reinforcement Learning NPC Team for Playing Domination Games

    Publication Year: 2010, Page(s):17 - 26
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (764 KB) | HTML iconHTML

    In this paper, we describe the design of reinforcement-learning-based domination team (RL-DOT), a nonplayer character (NPC) team for playing Unreal Tournament (UT) Domination games. In RL-DOT, there is a commander NPC and several soldier NPCs. The running process of RL-DOT consists of several decision cycles. In each decision cycle, the commander NPC makes a decision of troop distribution and, acc... View full abstract»

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  • Moving-Target Pursuit Algorithm Using Improved Tracking Strategy

    Publication Year: 2010, Page(s):27 - 39
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2293 KB) | HTML iconHTML

    Pursuing a moving target in modern computer games presents several challenges to situated agents, including real-time response, large-scale search space, severely limited computation resources, incomplete environmental knowledge, adversarial escaping strategy, and outsmarting the opponent. In this paper, we propose a novel tracking automatic optimization moving-target pursuit (TAO-MTP) algorithm e... View full abstract»

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  • Using Resource-Limited Nash Memory to Improve an Othello Evaluation Function

    Publication Year: 2010, Page(s):40 - 53
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (665 KB) | HTML iconHTML

    Finding the best strategy for winning a game using self-play or coevolution can be hindered by intransitivity among strategies and a changing fitness landscape. Nash Memory has been proposed as an archive for coevolution, to counter intransitivity and provide a more consistent fitness landscape. A lack of bounds on archive size might impede its use in a large, complex domain, such as the game of <... View full abstract»

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  • Modeling Player Experience for Content Creation

    Publication Year: 2010, Page(s):54 - 67
    Cited by:  Papers (41)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (844 KB) | HTML iconHTML

    In this paper, we use computational intelligence techniques to built quantitative models of player experience for a platform game. The models accurately predict certain key affective states of the player based on both gameplay metrics that relate to the actions performed by the player in the game, and on parameters of the level that was played. For the experiments presented here, a version of the ... View full abstract»

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  • 2010 IEEE World Congress on Computational Intelligence

    Publication Year: 2010, Page(s): 68
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2010, Page(s): C3
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  • IEEE Transactions on Computational Intelligence and AI in Games Information for authors

    Publication Year: 2010, Page(s): C4
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Aims & Scope

The IEEE Transactions on Computational Intelligence and AI in Games (T-CIAIG) publishes archival journal quality original papers in computational intelligence and related areas in artificial intelligence applied to games, including but not limited to videogames, mathematical games, human–computer interactions in games, and games involving physical objects. Emphasis is placed on the use of these methods to improve performance in and understanding of the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It also includes using games as a platform for building intelligent embedded agents for the real world. Papers connecting games to all areas of computational intelligence and traditional AI are considered.

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Meet Our Editors

Editor-in-Chief
Graham Kendall
The University of Nottingham
Jalan Broga, 43500 Semenyih
Selangor Darul Ehsan, Malaysia
Tel.: +6(30) 8924 8306
Fax: +6(30) 8924 8299
graham.kendall@nottingham.ac.uk
http://www.graham-kendall.com

Editorial Assistant
Wendy Knibb
wendy.knibb@gmail.com