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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

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

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  • Special Issue on Monte Carlo Techniques and Computer Go

    Page(s): 225 - 228
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  • Current Frontiers in Computer Go

    Page(s): 229 - 238
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 that in 9 × 9, computers are very close to the best human level, and can be improved easily for the opening book; whereas in 19 × 19, handicap 7 is not enough for the computers to win against top level professional players, due to some clearly understood (but not solved) weaknesses of the current algorithms. Applications far from the game of Go are also cited. Importantly, the first ever win of a computer against a 9th Dan professional player in 9 × 9 Go occurred in this event. View full abstract»

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  • Monte Carlo Tree Search in Lines of Action

    Page(s): 239 - 250
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    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 small to moderate branching factor, where efficient yet knowledge-rich evaluation functions can be applied effectively. In this paper, we describe an MCTS-based program for playing the game Lines of Action (LOA), which is a highly tactical slow-progression game exhibiting many of the properties difficult for MCTS. The program uses an improved MCTS variant that allows it to both prove the game-theoretical value of nodes in a search tree and to focus its simulations better using domain knowledge. This results in simulations superior in both handling tactics and ensuring game progression. Using the improved MCTS variant, our program is able to outperform even the world's strongest αβ-based LOA program. This is an important milestone for MCTS because the traditional game-tree search approach has been considered to be the better suited for playing LOA. View full abstract»

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  • Monte Carlo Tree Search in Hex

    Page(s): 251 - 258
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 alpha-beta Hex players. In this paper, we describe MoHex, the Monte Carlo tree search Hex player that won gold at the 2009 Computer Olympiad. Our main contributions to Monte Carlo tree search include using inferior cell analysis and connection strategy computation to prune the search tree. In particular, we run our random game simulations not on the actual game position, but on a reduced equivalent board. View full abstract»

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

    Page(s): 259 - 270
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    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 strong tournaments against other programs, and is competitive for 19 × 19 as well. This paper gives an overview of the development and current state of the FUEGO project. It describes the reusable components of the software framework and specific algorithms used in the Go engine. View full abstract»

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

    Page(s): 271 - 277
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 General Game Playing (GGP) competitions. We show that transposition tables improve UCT and that MAX is the best of these four algorithms. Using UCT+T, the program Ary won the 2009 GGP competition. MAX and NMC are slight improvements over this 2009 version. View full abstract»

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  • Evaluating Root Parallelization in Go

    Page(s): 278 - 287
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 experimental results with 64 central processing unit (CPU) cores show that majority voting outperforms average selection. Additionally, we show through an extensive analysis that root parallelization has limitations. View full abstract»

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  • Evaluation of Game Tree Search Methods by Game Records

    Page(s): 288 - 302
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    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 values with respect to win probabilities can be used to assess the performance of game tree search methods. A plot of win probabilities against evaluation values should have consistency and monotonicity if the evaluation values are produced by a good game tree search method. By inspecting whether the plot has the properties for some subset of positions, we can detect specific deficiencies in the game tree search method. We applied our method to Go, Shogi, and Chess, and by comparing the results with empirical understanding of the performance of various game tree search methods and with the results of self-plays, we show that our method is efficient and effective. View full abstract»

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

    Page(s): 303 - 309
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 that, in previous playouts, have been successful replies to immediately preceding moves. This paper presents a modification of this policy that not only remembers moves that recently succeeded but also immediately forgets moves that recently failed. This modification provides a large improvement in playing strength. We also show that responding to the previous two moves is superior to responding to the previous one move. Surprisingly, remembering the win rate of every reply performs much worse than simply remembering the last good reply (and indeed worse than not storing good replies at all). View full abstract»

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  • 2011 IEEE conference on computational intelligence and games

    Page(s): 310
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  • AIIDE 2011

    Page(s): 311
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  • Why we joined ... [advertisement]

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

    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

    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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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