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Computational Intelligence and AI in Games, IEEE Transactions on

Issue 4 • Date Dec. 2009

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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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  • Automatic Content Generation in the Galactic Arms Race Video Game

    Page(s): 245 - 263
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    Simulation and game content includes the levels, models, textures, items, and other objects encountered and possessed by players during the game. In most modern video games and in simulation software, the set of content shipped with the product is static and unchanging, or at best, randomized within a narrow set of parameters. However, ideally, if game content could be constantly and automatically renewed, players would remain engaged longer. This paper introduces two novel technologies that take steps toward achieving this ambition: 1) a new algorithm called content-generating NeuroEvolution of Augmenting Topologies (cgNEAT) is introduced that automatically generates graphical and game content while the game is played, based on the past preferences of the players, and 2) Galactic Arms Race (GAR), a multiplayer video game, is constructed to demonstrate automatic content generation in a real online gaming platform. In GAR, which is available to the public and playable online, players pilot space ships and fight enemies to acquire unique particle system weapons that are automatically evolved by the cgNEAT algorithm. A study of the behavior and results from over 1000 registered online players shows that cgNEAT indeed enables players to discover a wide variety of appealing content that is not only novel, but also based on and extended from previous content that they preferred in the past. Thus, GAR is the first demonstration of evolutionary content generation in an online multiplayer game. The implication is that with cgNEAT it is now possible to create applications that generate their own content to satisfy users, potentially reducing the cost of content creation and increasing entertainment value from single-player to massively multiplayer online games (MMOGs) with a constant stream of evolving content. View full abstract»

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  • Adaptive Experience Engine for Serious Games

    Page(s): 264 - 280
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1709 KB) |  | HTML iconHTML  

    Designing games that support knowledge and skill acquisition has become a promising frontier of education techniques, since games are able to capture the user concentration for long periods and can present users with realistic and compelling challenges. In this scenario, there is a need for scientific and engineering methods to build games not only as more realistic simulations of the physical world but as means to provide effective learning experiences. Abstracting state of the art serious games' (SGs) features, we propose a new design methodology for the sand box serious games (SBSGs) class, decoupling content from the delivery strategy during the gameplay. This methodology aims at making design more efficient and standardized in order to meet the growing demand for interactive learning. The methodology consists in modeling an SBSG as a hierarchy of tasks (e.g., missions) and specifies the requirements for a runtime scheduling policy that maximizes learning objectives in a full entertainment context. The policy is learned by an experience engine (EE) based on computational intelligence. In this approach, the domain-expert author focuses on the creation and semantic annotation of tasks. Tasks are put in a repository and can then be exploited by game designers who define the expected learning curve and other requirements about education and entertainment for the game. The task sequencing that aims at matching such specifications with the real user profile is then presented to the EE. The EE can operate also in absence of the specification of the learning curve, continuously adapting the game flow without aiming at the achievement of target knowledge levels predefined by the author. We have implemented an EE module based on genetic computation and reinforcement learning (RL) atop of a state-of-the-art game engine. Test results show that the EE is able to define in real-time missions that meet the requirements expressed by the author. View full abstract»

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  • Simulation of the Dynamics of Nonplayer Characters' Emotions and Social Relations in Games

    Page(s): 281 - 297
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    One of the main challenges faced by the video game industry is to give life to believable nonplayer characters (NPCs). Research shows that emotions play a key role in determining the behavior of individuals. In order to improve the believability of NPCs' behavior, we propose in this paper a model of the dynamics of emotions taking into account the personality and the social relations of the character. First, we present work from the literature on emotions, personality, and social relations in computer science and in human and social sciences. We focus on the influence of personality on the triggering of emotions, and the influence of emotions on the dynamics of social relations. Based on this work, we propose a dynamic model of the socioemotional state and its implementation as part of a tool for game programmers. This tool aims at the simulation of the evolution of emotions and social relations of NPCs based on their personality and roles. View full abstract»

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  • Query-Enabled Behavior Trees

    Page(s): 298 - 308
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    Artificial intelligence in games is typically used for creating player's opponents. Manual editing of intelligent behaviors for nonplayer characters (NPCs) of games is a cumbersome task that needs experienced designers. Our research aims to assist designers in this task. Behaviors typically use recurring patterns, so that experience and reuse are crucial aspects for behavior design. The use of hierarchical structures like hierarchical state machines, behavior trees (BTs), or hierarchical task networks, allows working on different abstraction levels reusing pieces from the more detailed levels. However, the static nature of the design process does not release the designer from the burden of completely specifying each behavior. Our approach applies case-based reasoning (CBR) techniques to retrieve and reuse stored behaviors represented as BTs. In this paper, we focus on dynamic retrieval and selection of behaviors taking into account the world state and the underlying goals. The global behavior of the NPC is dynamically built at runtime querying the CBR system. We exemplify our approach through a serious game, developed by our research group, with gameplay elements from first-person shooter (FPS) games. View full abstract»

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  • Generation of Adaptive Dilemma-Based Interactive Narratives

    Page(s): 309 - 326
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (485 KB) |  | HTML iconHTML  

    The generator of adaptive dilemma-based interactive narratives (GADIN) presented in this paper dynamically generates interactive narratives which are focused on dilemmas to create dramatic tension. The system is provided with knowledge of generic story actions and dilemmas based on those clichE??s encountered in many storytelling domains. The domain designer is only required to provide domain-specific information, for example, regarding characters and their relationships, locations, and actions. A planner creates sequences of actions that all lead to a dilemma for a character (who can be the user). The user interacts with the storyworld by making decisions on relevant dilemmas and by freely choosing their own actions. Using this input, the system chooses and adapts future storylines according to the user's past behavior. Previous interactive narrative systems often have content creation and ordering requirements that restrict the possibility for sustaining the dramatic interest of the narrative over a long time period. In addition, many of these systems are not easily transferable between domains. In this paper, the GADIN system is demonstrated to both be able to maintain the dramatic interest of generated narratives over a long time period and to have a core architecture that is applicable to any domain. View full abstract»

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  • Special issue on Monte Carlo techniques and computer go

    Page(s): 327
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  • IEEE Symposium on Computational Intelligence and Games (CIG2010)

    Page(s): 328
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  • 2009 Index IEEE Transactions on Computational Intelligence and AI in Games Vol. 1

    Page(s): 1 - 4
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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