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Computational Intelligence and AI in Games, IEEE Transactions on

Issue 4 • Date Dec. 2010

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  • 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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  • Special Issue on Monte Carlo Techniques and Computer Go

    Publication Year: 2010 , Page(s): 225 - 228
    Cited by:  Papers (6)
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  • Current Frontiers in Computer Go

    Publication Year: 2010 , Page(s): 229 - 238
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1262 KB) |  | HTML iconHTML  

    This paper presents the recent technical advances in Monte Carlo tree search (MCTS) for the game of Go, shows the many similarities and the rare differences between the current best programs, and reports the results of the Computer Go event organized at the 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE2009), in which four main Go programs played against top level humans. We see th... View full abstract»

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  • Monte Carlo Tree Search in Lines of Action

    Publication Year: 2010 , Page(s): 239 - 250
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (653 KB) |  | HTML iconHTML  

    The success of Monte Carlo tree search (MCTS) in many games, where αβ-based search has failed, naturally raises the question whether Monte Carlo simulations will eventually also outperform traditional game-tree search in game domains where αβ -based search is now successful. The forte of αβ-based search are highly tactical deterministic game domains with a... View full abstract»

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  • Monte Carlo Tree Search in Hex

    Publication Year: 2010 , Page(s): 251 - 258
    Cited by:  Papers (21)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1044 KB) |  | HTML iconHTML  

    Hex, the classic board game invented by Piet Hein in 1942 and independently by John Nash in 1948, has been a domain of AI research since Claude Shannon's seminal work in the 1950s. Until the Monte Carlo Go revolution a few years ago, the best computer Hex players used knowledge-intensive alpha-beta search. Since that time, strong Monte Carlo Hex players have appeared that are on par with the best ... View full abstract»

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  • Fuego—An Open-Source Framework for Board Games and Go Engine Based on Monte Carlo Tree Search

    Publication Year: 2010 , Page(s): 259 - 270
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (693 KB) |  | HTML iconHTML  

    FUEGO is both an open-source software framework and a state-of-the-art program that plays the game of Go. The framework supports developing game engines for full-information two-player board games, and is used successfully in a substantial number of projects. The FUEGO Go program became the first program to win a game against a top professional player in 9 × 9 Go. It has won a number of str... View full abstract»

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  • Combining UCT and Nested Monte Carlo Search for Single-Player General Game Playing

    Publication Year: 2010 , Page(s): 271 - 277
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (578 KB) |  | HTML iconHTML  

    Monte Carlo tree search (MCTS) has been recently very successful for game playing, particularly for games where the evaluation of a state is difficult to compute, such as Go or General Games. We compare nested Monte Carlo (NMC) search, upper confidence bounds for trees (UCT-T), UCT with transposition tables (UCT+T), and a simple combination of NMC and UCT+T (MAX) on single-player games of the past... View full abstract»

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  • Evaluating Root Parallelization in Go

    Publication Year: 2010 , Page(s): 278 - 287
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (676 KB) |  | HTML iconHTML  

    Parallelizing Monte Carlo tree search (MCTS) has been considered to be a way to improve the strength of Computer Go programs. In this paper, we analyze the performance of two root parallelization methods: the standard strategy based on average selection and our new strategy based on majority voting. As a starting code base, we used Fuego, which is one of the best programs available. Our experiment... View full abstract»

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  • Evaluation of Game Tree Search Methods by Game Records

    Publication Year: 2010 , Page(s): 288 - 302
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (766 KB) |  | HTML iconHTML  

    This paper presents a method of evaluating game tree search methods including standard min-max search with heuristic evaluation functions and Monte Carlo tree search, which recently achieved drastic improvements in the strength of Computer Go programs. The basic idea of this paper is to use an averaged win probability of positions having similar evaluation values. Accuracy measures of evaluation v... View full abstract»

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  • The Power of Forgetting: Improving the Last-Good-Reply Policy in Monte Carlo Go

    Publication Year: 2010 , Page(s): 303 - 309
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (518 KB) |  | HTML iconHTML  

    The dominant paradigm for programs playing the game of Go is Monte Carlo tree search. This algorithm builds a search tree by playing many simulated games (playouts). Each playout consists of a sequence of moves within the tree followed by many moves beyond the tree. Moves beyond the tree are generated by a biased random sampling policy. The recently published last-good-reply policy makes moves tha... View full abstract»

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  • 2011 IEEE conference on computational intelligence and games

    Publication Year: 2010 , Page(s): 310
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  • AIIDE 2011

    Publication Year: 2010 , Page(s): 311
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  • Why we joined ... [advertisement]

    Publication Year: 2010 , Page(s): 312
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  • 2010 Index IEEE Transactions on Computational Intelligence and AI in Games Vol. 2

    Publication Year: 2010 , Page(s): 1 - 4
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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.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Graham Kendall
The University of Nottingham
Jalan Broga, 43500 Semenyih
Selangor Darul Ehsan, Malaysia
Tel.: +6(30) 8924 8306
Fax: +6(30) 8924 8299
graham.kendall@nottingham.ac.uk
http://www.graham-kendall.com

Editorial Assistant
Wendy Knibb
wendy.knibb@gmail.com