2008 IEEE Symposium On Computational Intelligence and Games

15-18 Dec. 2008

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  • [Front cover]

    Publication Year: 2008, Page(s): c1
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  • Preface

    Publication Year: 2008, Page(s): ii
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  • Acknowledgements

    Publication Year: 2008, Page(s):iii - iv
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  • Table of contents

    Publication Year: 2008, Page(s):v - viii
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  • Organising Committee

    Publication Year: 2008, Page(s): ix
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  • Programme Committee

    Publication Year: 2008, Page(s):x - xi
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  • Keynote speakers

    Publication Year: 2008, Page(s):xii - xiii
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (149 KB)

    Provides an abstract for each of the keynote presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings. View full abstract»

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  • Tutorials

    Publication Year: 2008, Page(s):xiv - xvi
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (111 KB)

    Provides an abstract for each of the tutorial presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings. View full abstract»

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  • CIG'08: Competitions

    Publication Year: 2008, Page(s):xvii - xviii
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  • Special sessions

    Publication Year: 2008, Page(s):xix - xxi
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  • Contributors

    Publication Year: 2008, Page(s): xxii
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  • Author index

    Publication Year: 2008, Page(s):xxiii - xxxiv
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  • Investigating learning rates for evolution and temporal difference learning

    Publication Year: 2008, Page(s):1 - 7
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1055 KB) | HTML iconHTML

    Evidently, any learning algorithm can only learn on the basis of the information given to it. This paper presents a first attempt to place an upper bound on the information rates attainable with standard co-evolution and with TDL. The upper bound for TDL is shown to be much higher than for co-evolution. Under commonly used settings for learning to play Othello for example, TDL may have an upper bo... View full abstract»

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  • Adapting to human game play

    Publication Year: 2008, Page(s):8 - 15
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2466 KB) | HTML iconHTML

    No matter how good a computer player is, given enough time human players may learn to adapt to the strategy used, and routinely defeat the computer player. A challenging task is to mimic this human ability to adapt, and create a computer player that can adapt to its opposition's strategy. By having an adaptive strategy for a computer player, the challenge it provides is ongoing. Additionally, a co... View full abstract»

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  • Evolution of counter-strategies: Application of co-evolution to Texas Hold'em Poker

    Publication Year: 2008, Page(s):16 - 22
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (691 KB) | HTML iconHTML

    Texas Hold'em Poker is similar to other poker variants in that our decision process is controlled by outside factors as much as the cards themselves. Factors such as our seating position, stack size, the stage of the tournament and prior bets can strongly influence a players decision to bet or fold on a given hand of cards. Previous research has explored the use of these factors as means of bettin... View full abstract»

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  • Can opponent models aid poker player evolution?

    Publication Year: 2008, Page(s):23 - 30
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (7509 KB) | HTML iconHTML

    We investigate the impact of Bayesian opponent modeling upon the evolution of a player for a simplified poker game. Through the evolution of artificial neural networks using NEAT we create and compare players both utilizing and ignoring Bayesian opponent beliefs. We test the effectiveness of this model against various collections of dynamic and partially randomized opponents and find that using a ... View full abstract»

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  • Evolving opponent models for Texas Hold 'Em

    Publication Year: 2008, Page(s):31 - 38
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (507 KB) | HTML iconHTML

    Opponent models allow software agents to assess a multi-agent environment more accurately and therefore improve the agent's performance. This paper makes use of coarse approximations to game-theoretic player representations to improve the performance of software players in Limit Texas Hold 'Em poker. A 10-parameter model, intended to model a combination, or mixture, of various strategies is develo... View full abstract»

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  • An evaluation of models for predicting opponent positions in first-person shooter video games

    Publication Year: 2008, Page(s):39 - 46
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (566 KB) | HTML iconHTML

    A well-known Artificial Intelligence (AI) problem in video games is designing AI-controlled humanoid characters. It is desirable for these characters to appear both skillful and believably human-like. Many games address the former objective by providing their agents with unfair advantages. Although challenging, these agents are frustrating to humans who perceive the AI to be cheating. In this pape... View full abstract»

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  • Dynamic formations in real-time strategy games

    Publication Year: 2008, Page(s):47 - 54
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (934 KB) | HTML iconHTML

    Current approaches to organising units in strategic video games are typically implemented via static formations. Static formations are not capable of adapting effectively to opponent tactics. In this paper we discuss an approach to organising units by learning the effectiveness of a formation in actual play, and directly applying learned formations according to the classification of the opponent p... View full abstract»

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  • Dealing with fog of war in a Real Time Strategy game environment

    Publication Year: 2008, Page(s):55 - 62
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (683 KB) | HTML iconHTML

    Bots for real time strategy (RTS) games provide a rich challenge to implement. A bot controls a number of units that may have to navigate in a partially unknown environment, while at the same time search for enemies and coordinate attacks to fight them down. It is often the case that RTS AIs cheat in the sense that they get perfect information about the game world to improve the performance of the... View full abstract»

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  • Intelligent anti-grouping in real-time strategy games

    Publication Year: 2008, Page(s):63 - 70
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (8842 KB) | HTML iconHTML

    Assembling suitable groups of fighting units to combat incoming enemy groups is a tactical necessity in real-time strategy (RTS) games. Furthermore it heavily influences future strategic decisions like unit building. Here, we demonstrate how to efficiently (offline) solve the problem of finding matches for the current enemy group(s) based on self-organizing maps (SOMs), powered by a simple evoluti... View full abstract»

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  • Intelligent moving of groups in real-time strategy games

    Publication Year: 2008, Page(s):71 - 78
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (641 KB) | HTML iconHTML

    This paper investigates the intelligent moving and path-finding of groups in real-time strategy (RTS) games exemplified by the open source game Glest. We utilize the technique of flocking for achieving a smooth and natural movement of a group of units and expect grouping to decrease the amount of unit losses in RTS games. Furthermore, we present a setting in which flocking will improve the game pr... View full abstract»

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  • Rapid adaptation of video game AI

    Publication Year: 2008, Page(s):79 - 86
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (968 KB) | HTML iconHTML

    Current approaches to adaptive game AI require either a high quality of utilised domain knowledge, or a large number of adaptation trials. These requirements hamper the goal of rapidly adapting game AI to changing circumstances. In an alternative, novel approach, domain knowledge is gathered automatically by the game AI, and is immediately (i.e., without trials and without resource-intensive learn... View full abstract»

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  • Real-time challenge balance in an RTS game using rtNEAT

    Publication Year: 2008, Page(s):87 - 94
    Cited by:  Papers (18)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3378 KB) | HTML iconHTML

    This paper explores using the NEAT and rtNEAT neuro-evolution methodologies to generate intelligent opponents in real-time strategy (RTS) games. The main objective is to adapt the challenge generated by the game opponents to match the skill of a player in real-time, ultimately leading to a higher entertainment value perceived by a human player of the game. Results indicate the effectiveness of NEA... View full abstract»

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  • Friendly partner system of poker game with facial expressions

    Publication Year: 2008, Page(s):95 - 102
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (538 KB) | HTML iconHTML

    This paper aims at the construction of a partner system with facial expressions, which plays a seven-card stud poker game with a human player against an opponent player. If a human player needs some advice on a game, the partner system has various facial expressions according to the current situation. Fuzzy theory is applied to decision-making part in the partner system and neural network is appli... View full abstract»

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