IEEE Transactions on Computational Intelligence and AI in Games

Issue 1 • March 2017

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Displaying Results 1 - 12 of 12
  • Table of Contents

    Publication Year: 2017, Page(s): C1
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  • IEEE Transactions on Computational Intelligence and AI in Games

    Publication Year: 2017, Page(s): C2
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  • A Hyperheuristic Methodology to Generate Adaptive Strategies for Games

    Publication Year: 2017, Page(s):1 - 10
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1845 KB) | HTML iconHTML

    Hyperheuristics have been successfully applied in solving a variety of computational search problems. In this paper, we investigate a hyperheuristic methodology to generate adaptive strategies for games. Based on a set of low-level heuristics (or strategies), a hyperheuristic game player can generate strategies which adapt to both the behavior of the co-players and the game dynamics. By using a si... View full abstract»

    Open Access
  • Opponent Modeling by Expectation–Maximization and Sequence Prediction in Simplified Poker

    Publication Year: 2017, Page(s):11 - 24
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3111 KB) | HTML iconHTML

    We consider the problem of learning an effective strategy online in a hidden information game against an opponent with a changing strategy. We want to model and exploit the opponent and make three proposals to do this; first, to infer its hidden information using an expectation-maximization (EM) algorithm; second, to predict its actions using a sequence prediction method; and third, to simulate ga... View full abstract»

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  • Neuroevolution in Games: State of the Art and Open Challenges

    Publication Year: 2017, Page(s):25 - 41
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1073 KB) | HTML iconHTML

    This paper surveys research on applying neuroevolution (NE) to games. In neuroevolution, artificial neural networks are trained through evolutionary algorithms, taking inspiration from the way biological brains evolved. We analyze the application of NE in games along five different axes, which are the role NE is chosen to play in a game, the different types of neural networks used, the way these n... View full abstract»

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  • Creating Affective Autonomous Characters Using Planning in Partially Observable Stochastic Domains

    Publication Year: 2017, Page(s):42 - 62
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2017 KB) | HTML iconHTML

    The ability to reason about and respond to their own emotional states can enhance the believability of Non-Player Characters (NPCs). In this paper, we use a Partially Observable Markov Decision Process (POMDP)-based framework to model emotion over time. A two-level appraisal model, involving quick and reactive vs. slow and deliberate appraisals, is proposed for the creation of affective autonomous... View full abstract»

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  • Rapid Skill Capture in a First-Person Shooter

    Publication Year: 2017, Page(s):63 - 75
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1269 KB) | HTML iconHTML

    Various aspects of computer game design, including adaptive elements of game levels, characteristics of “bot” behavior, and player matching in multiplayer games, would ideally be sensitive to a player's skill level. Yet, while game difficulty and player learning have been explored in the context of games, there has been little work analyzing skill per se, and how this is related to t... View full abstract»

    Open Access
  • Partition Search Revisited

    Publication Year: 2017, Page(s):76 - 87
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1505 KB) | HTML iconHTML

    Partition search is a form of game search, proposed by Matthew L. Ginsberg in 1996, who wrote that the method “incorporates dependency analysis, allowing substantial reductions in the portion of the tree that needs to be expanded.” In this paper, some improvements of the partition search algorithm are proposed. The effectiveness of the most important extension we contribute, which we... View full abstract»

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  • Only-One-Victor Pattern Learning in Computer Go

    Publication Year: 2017, Page(s):88 - 102
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2839 KB) | HTML iconHTML

    Automatically acquiring domain knowledge from professional game records, a kind of pattern learning, is an attractive and challenging issue in computer Go. This paper proposes a supervised learning method, by introducing a new generalized Bradley-Terry model, named Only-One-Victor, to learn patterns from game records. Basically, our algorithm applies the same idea with Elo rating algorithm, which ... View full abstract»

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  • Automatic Classification of Player Complaints in Social Games

    Publication Year: 2017, Page(s):103 - 108
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1765 KB) | HTML iconHTML

    Artificial intelligence and machine learning techniques are not only useful for creating plausible behaviors for interactive game elements, but also for the analysis of the players to provide a better gaming environment. In this paper, we propose a novel framework for automatic classification of player complaints in a social gaming platform. We use features that describe both parties of the compla... View full abstract»

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  • IEEE Computational Intelligence Society

    Publication Year: 2017, Page(s): C3
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  • Information for Authors

    Publication Year: 2017, 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