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Computational Intelligence and AI in Games, IEEE Transactions on

Issue 1 • Date March 2009

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  • Table of contents

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

    Page(s): C2
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    Freely Available from IEEE
  • Computational Intelligence and AI in Games: A New IEEE Transactions

    Page(s): 1 - 3
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (252 KB)  

    The author first provides an overview of computational intelligence and AI in games. Then he describes the new IEEE Transactions, which will publish archival quality original papers in all aspects of computational intelligence and AI related to all types of games. To name some examples, these include computer and video games, board games, card games, mathematical games, games that model economies or societies, serious games with educational and training applications, and games involving physical objects such as robot football and robotic car racing. Emphasis will also be 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 will also include using games as a platform for building intelligent embedded agents for real-world applications. The journal builds on a scientific community that has already been active in recent years with the development of new conference series such as the IEEE Symposium on Computational Intelligence in Games (CIG) and Artificial Intelligence and Interactive Digital Entertainment (AIIDE), as well as special issues on games in journals such as the IEEE Transactions on Evolutionary Computation. When setting up the journal, a decision was made to include both artificial intelligence (AI) and computational intelligence (CI) in the title. AI seeks to simulate intelligent behavior in any way that can be programmed effectively. Some see the field of AI as being all-inclusive, while others argue that there is nothing artificial about real intelligence as exhibited by higher mammals. View full abstract»

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  • CadiaPlayer: A Simulation-Based General Game Player

    Page(s): 4 - 15
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (835 KB) |  | HTML iconHTML  

    The aim of general game playing (GGP) is to create intelligent agents that can automatically learn how to play many different games at an expert level without any human intervention. The traditional design model for GGP agents has been to use a minimax-based game-tree search augmented with an automatically learned heuristic evaluation function. The first successful GGP agents all followed that approach. In this paper, we describe CadiaPlayer, a GGP agent employing a radically different approach: instead of a traditional game-tree search, it uses Monte Carlo simulations for its move decisions. Furthermore, we empirically evaluate different simulation-based approaches on a wide variety of games, introduce a domain-independent enhancement for automatically learning search-control knowledge to guide the simulation playouts, and show how to adapt the simulation searches to be more effective in single-agent games. CadiaPlayer has already proven its effectiveness by winning the 2007 and 2008 Association for the Advancement of Artificial Intelligence (AAAI) GGP competitions. View full abstract»

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  • Effective and Diverse Adaptive Game AI

    Page(s): 16 - 27
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1762 KB) |  | HTML iconHTML  

    Adaptive techniques tend to converge to a single optimum. For adaptive game AI, such convergence is often undesirable, as repetitive game AI is considered to be uninteresting for players. In this paper, we propose a method for automatically learning diverse but effective macros that can be used as components of adaptive game AI scripts. Macros are learned by a cross-entropy method (CEM). This is a selection-based optimization method that, in our experiments, maximizes an interestingness measure. We demonstrate the approach in a computer role-playing game (CRPG) simulation with two duelling wizards, one of which is controlled by an adaptive game AI technique called ldquodynamic scripting.rdquo Our results show that the macros that we learned manage to increase both adaptivity and diversity of the scripts generated by dynamic scripting, while retaining playing strength. View full abstract»

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  • Extending the Applicability of Pattern and Endgame Databases

    Page(s): 28 - 38
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (763 KB) |  | HTML iconHTML  

    For most high-performance two-player game programs, a significant amount of time is devoted to developing the evaluation function. An important issue in this regard is how to take advantage of a large memory. For some two-player games, endgame databases have been an effective way of reducing search effort and introducing accurate values into the search. For some one-player games (single-agent domains or puzzles), pattern databases have been effective at improving the quality of the heuristic values used in a search. This paper introduces new ways to extend the utility of pattern and endgame databases. Through the use of abstraction: (1) single-agent pattern databases can be applied to two- or more-player games; knowledge of the capabilities of one player (being oblivious to the opponent) can be an effective evaluation function for a class of game domains, and (2) endgame database positions can be viewed as an abstraction of more complicated positions; database lookups can be used as evaluation function features. These ideas are illustrated using the games of Chinese Checkers, Chess, and Thief and Police. For each domain, even small databases can be used to produce strong game play. This research has relevance to the recent interest in building general game-playing (GGP) programs. For two- or more-player applications where pattern and/or endgame databases can be built, abstraction can be used to automatically construct an evaluation function. View full abstract»

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  • Lightweight Procedural Animation With Believable Physical Interactions

    Page(s): 39 - 49
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (423 KB) |  | HTML iconHTML  

    In this paper, we describe Twig, a fast, AI-friendly procedural animation system that supports easy authoring of new behaviors. The system provides a simplified dynamic simulation that is specifically designed to be easy to control. Characters are controlled by applying external forces directly to body parts, rather than by simulating joint torques. This ldquopuppetry-stylerdquo of control provides the simplicity of kinematic control within an otherwise dynamic simulation. Although less realistic than motion capture or full biomechanical simulation, Twig produces compelling, responsive character behavior. Moreover, it is fast, stable, supports believable physical interactions between characters such as hugging, punching, and dragging, and makes it easy to author new behaviors. View full abstract»

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  • Effects of Shared Perception on the Evolution of Squad Behaviors

    Page(s): 50 - 62
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1374 KB) |  | HTML iconHTML  

    As the nonplayable characters (NPCs) of squad-based shooter computer games share a common goal, they should work together in teams and display cooperative behaviors that are tactically sound. Our research examines genetic programming (GP) as a technique to automatically develop effective squad behaviors for shooter games. GP has been used to evolve teams capable of defeating a single powerf.ul enemy agent in a number of environments without the use of any explicit team communication. This paper is an extension of our paper presented at the 2008 Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE'08). Its aim is to explore the effects of shared perception on the evolution of effective squad behaviors. Thus, NPCs are given the ability to explicitly communicate their perceived information during evolution. The results show that the explicit communication of perceived information between team members enables an improvement in average team effectiveness. View full abstract»

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  • Learning Finite-State Machine Controllers From Motion Capture Data

    Page(s): 63 - 72
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (580 KB) |  | HTML iconHTML  

    With characters in computer games and interactive media increasingly being based on real actors, the individuality of an actor's performance should not only be reflected in the appearance and animation of the character but also in the AI that governs the character's behavior and interactions with the environment. Machine learning methods applied to motion capture data provide a way of doing this. This paper presents a method for learning the parameters of a finite-state machine (FSM) controller. The method learns both the transition probabilities of the FSM and also how to select animations based on the current state. View full abstract»

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  • The Computational Intelligence of MoGo Revealed in Taiwan's Computer Go Tournaments

    Page(s): 73 - 89
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3878 KB) |  | HTML iconHTML  

    In order to promote computer Go and stimulate further development and research in the field, the event activities, Computational Intelligence Forum and World 9times9 Computer Go Championship, were held in Taiwan. This study focuses on the invited games played in the tournament Taiwanese Go players versus the computer program MoGo held at the National University of Tainan (NUTN), Tainan, Taiwan. Several Taiwanese Go players, including one 9-Dan (9D) professional Go player and eight amateur Go players, were invited by NUTN to play against MoGo from August 26 to October 4, 2008. The MoGo program combines all-moves-as-first (AMAF)/rapid action value estimation (RAVE) values, online "upper confidence tree (UCT)-like" values, offline values extracted from databases, and expert rules. Additionally, four properties of MoGo are analyzed including: (1) the weakness in corners, (2) the scaling over time, (3) the behavior in handicap games, and (4) the main strength of MoGo in contact fights. The results reveal that MoGo can reach the level of 3 Dan (3D) with: (1) good skills for fights, (2) weaknesses in corners, in particular, for "semeai" situations, and (3) weaknesses in favorable situations such as handicap games. It is hoped that the advances in AI and computational power will enable considerable progress in the field of computer Go, with the aim of achieving the same levels as computer chess or Chinese chess in the future. View full abstract»

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  • Please Join Us at Stanford for AIIDE-09!

    Page(s): 90
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    Freely Available from IEEE
  • IEEE Transactions on Autonomous Mental Development (TAMD)

    Page(s): 91
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    Freely Available from IEEE
  • IEEE Symposium on Computational Intelligence & Games (CIG-2009)

    Page(s): 92
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    Freely Available from IEEE
  • IEEE Computational Intelligence Society Information

    Page(s): C3
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    Freely Available from IEEE
  • IEEE Transactions on Computational Intelligence and AI in Games Information for authors

    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.

Full Aims & Scope

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