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Computational Intelligence and AI in Games, IEEE Transactions on

Issue 4 • Date Dec. 2011

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  • Table of contents

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

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  • Dynamic Game Difficulty Scaling Using Adaptive Behavior-Based AI

    Page(s): 289 - 301
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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, an adaptive game AI that can scale the difficulty of the game according to the proficiency of the player has greater potential to customize a personalized and entertaining game experience compared to a static game AI. In particular, dynamic game difficulty scaling refers to the use of an adaptive game AI that performs game adaptations in real time during the game session. This paper presents two adaptive algorithms that use ideas from reinforcement learning and evolutionary computation to improve player satisfaction by scaling the difficulty of the game AI while the game is being played. The effects of varying the learning and mutation rates are examined and a general rule of thumb for the parameters is proposed. The proposed algorithms are demonstrated to be capable of matching its opponents in terms of mean scores and winning percentages. Both algorithms are able to generalize well to a variety of opponents. View full abstract»

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

    Page(s): 302 - 315
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    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 computationally simple as players do not need to recompute the sequential strategies from the data collected to date. Pursuing the proposed efficient bidding (EB) algorithm, players monitor their relative performance in the course of the game and submit their bids based on their current estimate of the market condition. We prove the stability and robustness of the proposed strategies and show that they dominate myopic and random bidding strategies using an experiment in search engine marketing. View full abstract»

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

    Page(s): 316 - 331
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 respect to him. There is no definite answer as to which approach is better, and their main shortcoming is that their strategy is fixed. We therefore suggest the MaxN-paranoid mixture (MP-Mix) algorithm: a multiplayer adversarial search that switches search strategies according to the game situation. The MP-mix algorithm examines the current situation and decides whether the root player should follow the MaxN principle, the paranoid principle, or the newly presented directed offensive principle. To evaluate our new algorithm, we performed extensive experimental evaluation on three multiplayer domains: Hearts, Risk, and Quoridor. In addition, we also introduce the opponent impact (OI) measure, which measures the players' ability to impede their opponents' efforts, and show its relation to the relative performance of the MP-mix strategy. The results show that our MP-mix strategy significantly outperforms MaxN and paranoid in various settings in all three games. View full abstract»

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  • The 2010 Mario AI Championship: Level Generation Track

    Page(s): 332 - 347
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    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 presents the rules of the competition, the software used, the scoring procedure, the submitted level generators, and the results of the competition. We also discuss what can be learned from this competition, both about organizing procedural content generation competitions and about automatically generating levels for platform games. The paper is coauthored by the organizers of the competition (the first three authors) and the competitors. View full abstract»

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

    Page(s): 348 - 360
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    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 in which the strategies of the participants are randomly chosen from a set of representative strategies. Statistics are gathered to evaluate the performance of each strategy. With this approach, the conditions for some well-known strategies to win a round-robin IPD competition are analyzed. We show that a strategy that uses simple rule-based identification mechanisms to explore and exploit the opponent outperforms well-known strategies such as tit-for-tat (TFT) in most round-robin competitions. Group strategies have an advantage over nongroup strategies in evolutionary IPD competitions. Group strategies adopt different strategies in interacting with kin members and nonkin members. A simple group strategy, Clique, which cooperates only with kin members, performs well in competing against well-known IPD strategies. We introduce several group strategies developed by combining Clique with winning strategies from round-robin competitions and evaluate their performance by adapting three parameters: sole survivor rate, extinction rate, and survival time. Simulation results show that these group strategies outperform well-known IPD strategies in evolutionary IPD competitions. View full abstract»

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  • IEEE Computational Intelligence Society Information

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

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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
Simon M. Lucas
School of Computer Science and Electronic Engineering
University of Essex
Colchester, Essex  CO43SQ, U.K.
sml@essex.ac.uk
Phone:+44 1206 872 048
Fax:+44 1206 872 788