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Computational Intelligence and AI in Games, IEEE Transactions on

Issue 3 • Date Sept. 2010

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Displaying Results 1 - 9 of 9
  • 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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  • Automatic Generation of Game Level Solutions as Storyboards

    Publication Year: 2010 , Page(s): 149 - 161
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4418 KB) |  | HTML iconHTML  

    Game programmers rely on artificial intelligence techniques to encode characters' behaviors initially specified by game designers. Although significant efforts have been made to assist their collaboration, the formalization of behaviors remains a time-consuming process during the early stages of game development. We propose an authoring tool allowing game designers to formalize, visualize, modify, and validate game level solutions in the form of automatically generated storyboards. This system uses planning techniques to produce a level solution consistent with gameplay constraints. The main planning agent corresponds to the player character, and the system uses the game actions as planning operators and level objectives as goals to plan the level solutions. Generated solutions are presented as 2-D storyboards similar to comic strips. We present in this paper the first version of a fully implemented prototype as well as examples of generated storyboards, adapted from the original design documents of the blockbuster game Hitman. View full abstract»

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  • Game Bot Detection via Avatar Trajectory Analysis

    Publication Year: 2010 , Page(s): 162 - 175
    Cited by:  Papers (10)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1512 KB) |  | HTML iconHTML  

    The objective of this work is to automatically detect the use of game bots in online games based on the trajectories of account users. Online gaming has become one of the most popular Internet activities in recent years, but cheating activity, such as the use of game bots, has increased as a consequence. Generally, the gaming community disapproves of the use of bots, as users may obtain unreasonable rewards without making corresponding efforts. However, game bots are hard to detect because they are designed to simulate human game playing behavior and they follow game rules exactly. Existing methods cannot solve the problem as the differences between bot and human trajectories are generally hard to describe. In this paper, we propose a method for detecting game bots based on some dissimilarity measurements between the trajectories of either bots or human users. The measurements are combined with manifold learning and classification techniques for detection; and the approach is generalizable to any game in which avatars' movements are controlled by the players directly. Through real-life data traces, we observe that the trajectories of bots and humans are very different. Since certain human behavior patterns are difficult to mimic, the characteristic can be used as a signature for bot detection. To evaluate the proposed scheme's performance, we conduct a case study of a popular online game called Quake 2. The results show that the scheme can achieve a high detection rate or classification accuracy on a short trace of several hundred seconds. View full abstract»

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  • Learning to Drive in the Open Racing Car Simulator Using Online Neuroevolution

    Publication Year: 2010 , Page(s): 176 - 190
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (733 KB) |  | HTML iconHTML  

    In this paper, we applied online neuroevolution to evolve nonplayer characters for The Open Racing Car Simulator (TORCS). While previous approaches allowed online learning with performance improvements during each generation, our approach enables a finer grained online learning with performance improvements within each lap. We tested our approach on three tracks using two methods of online neuroevolution (NEAT and rtNEAT) combined with four evaluation strategies ( -greedy, -greedy-improved, softmax, and interval-based) taken from the literature. We compared the eight resulting configurations on several driving tasks involving the learning of a driving behavior for a specific track, its adaptation to a new track, and the generalization capability to unknown tracks. The results we present show that, notwithstanding the several challenges that online learning poses, our approach 1) can successfully evolve drivers from scratch, 2) can also be used to transfer evolved knowledge to other tracks, and 3) can generalize effectively producing controllers that can drive on difficult unseen tracks. Our results also suggest that the approach performs better when coupled with online NEAT and also indicate that -greedy-improved and softmax are generally better than the other evaluation strategies. A comparison with typical offline neuroevolution suggests that online neuroevolution can be competitive and even outperform traditional offline approaches on more difficult tracks while providing all the interesting features of online learning. Overall, we believe that this study may represent an initial step toward the application of online neuroevolution in games. View full abstract»

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  • Relevance-Zone-Oriented Proof Search for Connect6

    Publication Year: 2010 , Page(s): 191 - 207
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1798 KB) |  | HTML iconHTML  

    Wu and Huang (Advances in Computer Games, pp. 180-194, 2006) presented a new family of k-in-a-row games, among which Connect6 (a kind of six-in-a-row) attracted much attention. For Connect6 as well as the family of k -in-a-row games, this paper proposes a new threat-based proof search method, named relevance-zone-oriented proof (RZOP) search, developed from the lambda search proposed by Thomsen (Int. Comput. Games Assoc. J., vol. 23, no. 4, pp. 203-217, 2000). The proposed RZOP search is a novel, general, and elegant method of constructing and promoting relevance zones. Using this method together with a proof number search, this paper solved effectively and successfully many new Connect6 game positions, including several Connect6 openings, especially the Mickey Mouse opening, which used to be one of the popular openings before we solved it. View full abstract»

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  • The Parametrized Probabilistic Finite-State Transducer Probe Game Player Fingerprint Model

    Publication Year: 2010 , Page(s): 208 - 224
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1545 KB) |  | HTML iconHTML  

    Fingerprinting operators generate functional signatures of game players and are useful for their automated analysis independent of representation or encoding. The theory for a fingerprinting operator which returns the length-weighted probability of a given move pair occurring from playing the investigated agent against a general parametrized probabilistic finite-state transducer (PFT) is developed, applicable to arbitrary iterated games. Results for the distinguishing power of the 1-state opponent model, uniform approximability of fingerprints of arbitrary players, analyticity and Lipschitz continuity of fingerprints for logically possible players, and equicontinuity of the fingerprints of bounded-state probabilistic transducers are derived. Algorithms for the efficient computation of special instances are given; the shortcomings of a previous model, strictly generalized here from a simple projection of the new model, are explained in terms of regularity condition violations, and the extra power and functional niceness of the new fingerprints demonstrated. The 2-state deterministic finite-state transducers (DFTs) are fingerprinted and pairwise distances computed; using this the structure of DFTs in strategy space is elucidated. View full abstract»

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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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Editor-in-Chief
Graham Kendall