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IEEE Transactions on Computational Intelligence and AI in Games

Issue 4 • Dec. 2011

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Displaying Results 1 - 9 of 9
  • Table of contents

    Publication Year: 2011, Page(s): C1
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  • IEEE Transactions on Computational Intelligence and AI in Games publication information

    Publication Year: 2011, Page(s): C2
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  • Dynamic Game Difficulty Scaling Using Adaptive Behavior-Based AI

    Publication Year: 2011, Page(s):289 - 301
    Cited by:  Papers (11)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1733 KB) | HTML iconHTML

    Games are played by a wide variety of audiences. Different individuals will play with different gaming styles and employ different strategic approaches. This often involves interacting with nonplayer characters that are controlled by the game AI. From a developer's standpoint, it is important to design a game AI that is able to satisfy the variety of players that will interact with the game. Thus,... View full abstract»

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  • A Robust Learning Approach to Repeated Auctions With Monitoring and Entry Fees

    Publication Year: 2011, Page(s):302 - 315
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2561 KB) | HTML iconHTML

    In this paper, we present a strategic bidding framework for repeated auctions with monitoring and entry fees. We motivate and formally define the desired properties of our framework and present a recursive bidding algorithm, according to which buyers learn to avoid submitting bids in stages where they have a relatively low chance of winning the auctioned item. The proposed bidding strategies are c... View full abstract»

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  • The MP-MIX Algorithm: Dynamic Search Strategy Selection in Multiplayer Adversarial Search

    Publication Year: 2011, Page(s):316 - 331
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1554 KB) | HTML iconHTML

    When constructing a search tree for multiplayer games, there are two basic approaches to propagating the opponents' moves. The first approach, which stems from the MaxN algorithm, assumes each opponent will follow his highest valued heuristic move. In the second approach, the paranoid algorithm, the player prepares for the worst case by assuming the opponents will select the worst move with respec... View full abstract»

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  • The 2010 Mario AI Championship: Level Generation Track

    Publication Year: 2011, Page(s):332 - 347
    Cited by:  Papers (19)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1945 KB) | HTML iconHTML

    The Level Generation Competition, part of the IEEE Computational Intelligence Society (CIS)-sponsored 2010 Mario AI Championship, was to our knowledge the world's first procedural content generation competition. Competitors participated by submitting level generators - software that generates new levels for a version of Super Mario Bros tailored to individual players' playing style. This paper pre... View full abstract»

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  • Engineering Design of Strategies for Winning Iterated Prisoner's Dilemma Competitions

    Publication Year: 2011, Page(s):348 - 360
    Cited by:  Papers (11)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1779 KB) | HTML iconHTML

    In this paper, we investigate winning strategies for round-robin iterated Prisoner's Dilemma (IPD) competitions and evolutionary IPD competitions. Since the outcome of a single competition depends on the composition of the population of participants, we propose a statistical evaluation methodology that takes into account outcomes across varying compositions. We run several series of competitions i... View full abstract»

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  • IEEE Computational Intelligence Society Information

    Publication Year: 2011, Page(s): C3
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  • IEEE Transactions on Computational Intelligence and AI in Games information for authors

    Publication Year: 2011, 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