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Computer Games Workshop 2007 Computer Games Workshop 2007 Amsterdam, June 15{17, 2007 MAIN SPONSORS Preface We are pleased to present the proceedings of the Computer Games Workshop 2007, Amsterdam, June 15{17, 2007. This workshop will be held in conjunc- tion with the 12th Computer Olympiad and the 15th World Computer-Chess Championship. Although the announcement was quite late, we were pleased to receive no less than 24 contributions. After a \light" refereeing process 22 papers were accepted. We believe that they present a nice overview of state-of-the-art research in the ¯eld of computer games. The 22 accepted papers can be categorized into ¯ve groups, according to the type of games used. Chess and Chess-like Games In this group we have included two papers on Chess, one on Kriegspiel, and three on Shogi (Japanese Chess). Matej Guid and Ivan Bratko investigate in Factors A®ecting Diminishing Returns for Searching Deeper the phenomenon of diminishing returns for addi- tional search e®ort. Using the chess programs Crafty and Rybka on a large set of grandmaster games, they show that diminishing returns depend on (a) the value of positions, (b) the quality of the evaluation function, and (c) the phase of the game and the amount of material on the board. Matej Guid, Aritz P¶erez,and Ivan Bratko in How Trustworthy is Crafty's Analysis of Chess Champions? again used Crafty in an attempt at an objective assessment of the strength of chess grandmasters of di®erent times. They show that their analysis is trustworthy, and hardly depends on the strength of the chess program used, the search depth applied, or the size of the sets of positions used. In the paper Moving in the Dark: Progress through Uncertainty in Kriegspiel Andrea Bolognesi and Paolo Ciancarini show their latest results on Kriegspiel. This is an incomplete-information chess-variant were the player does not see the opponent's pieces. They tested simple Kriegspiel endings and reveal how their program is able to deal with the inherent uncertainty. Kosuke Tosaka, Asuka Takeuchi, Shunsuke Soeda, and Hitoshi Matsubara propose in Extracting Important Features by Analyzing Game Records in Shogi a method for feature extraction for di®erent groups of Shogi players, using a statistical analysis of game records. They explain how they were able to achieve high discriminant rates in their analysis. Takeshi Ito introduces the idea of Sel¯sh Search in Shogi. This search process mimics human-like play, based on intuition and linear search. He discusses the main characteristics of such a system, and demonstrates its application on some sample Shogi positions. In Context Killer Heuristic and its Application to Computer Shogi Junichi Hashimoto, Tsuyoshi Hashimoto, and Hiroyuki Iida propose a new killer heuris- tic. Unlike the standard killer heuristic, context killer moves are based on what they call the context-based similarity of positions. Self-play experiments per- formed in the domain of Shogi demonstrate the e®ectiveness of the proposed idea. Go It is clear that the complex game of Go attracts more and more attention from researchers in Arti¯cial Intelligence. Being one of the hardest traditional games for computers, many research groups undertake the challenge of building strong Go programs and Go tools. Especially intriguing is the success of Monte-Carlo simulations as incorporated in most of the current top programs. In the paper Checking Life-and-Death Problems in Go. I: The Program Scan- LD, Thomas Wolf and Lei Shen present their program, which checks solutions of life-and-death problems for correctness. After discussing the di®erent types of checks performed by their program, they give statistics resulting from checking a 500-problem Tsume-Go book. They illustrate the mistakes that have been found by examples. A complete list is available on-line. In Introducing Playing Style to Computer Go, Esa A. Seuranen discusses the weaknesses in the current approaches in computer Go and proposes a design, aimed at overcoming some of the shortcomings. In order to achieve this, a posi- tion is subdivided into subgames, having local purposes. According to a playing style the best move is chosen from the best moves for the subgames. Tristan Cazenave and Nicolas Jouandeau present in their paper On the Par- allelization of UCT three parallel algorithms for UCT. They all three improve the results for programs in the ¯eld of 9 £ 9 Go. In Monte-Carlo Go with Knowledge-Guided Simulations, Keh-Hsun Chen and Peigang Zhang identify important Go domain knowledge, suited to be used in Monte-Carlo Go. They designed knowledge-guided simulations to be combined with the UCT algorithm, for the 9 £ 9 Go domain. Extensive tests against three top programs demonstrate the merit of this approach. R¶emiCoulom in Computing Elo Ratings of Move Patterns in the Game of Go demonstrates another method to incorporate domain knowledge into Go- playing programs. He presents a new Bayesian technique for supervised learning of move patterns from game records, based on a generalization of Elo ratings. Experiments with a Monte-Carlo program show that this algorithm outperforms most previous pattern-learning algorithms. Jahn-Takeshi Saito, Mark H.M. Winands, Jos W.H.M. Uiterwijk, and H. Jaap van den Herik propose in their paper Grouping Nodes for Monte-Carlo Tree Search another idea to enhance Monte-Carlo search in Go programs. They propose to distinguish two types of nodes in a game tree, move nodes and group nodes. A technique, called Alternating-Layer UCT, is designed for managing both types of nodes in a tree consisting of alternating layers of move nodes and group nodes. Self-play experiments show that group nodes can improve the playing strength of a Monte-Carlo program. Other Abstract Games Next to the above-mentioned papers dealing with the domains of Chess and Go, six more papers investigate other abstract games. In An E±cient Approach to Solve Mastermind Optimally, the authors Li- Te Huang, Shan-Tai Chen, Shih-Chieh Huang, and Shun-Shii Lin deal with the well-known Mastermind game. They propose a new backtracking algorithm with branch-and-bound pruning (BABBP). This algorithm is more e±cient than pre- vious algorithms and can presumably be applied to other games as well. James Glenn, Haw-ren Fang, and Clyde P. Kruskal in A Retrograde Approx- imation Algorithm for Two-Player Can't Stop investigate the two-player version of Can't Stop, a game designed by Sid Sackson. They present a retrograde ap- proximation algorithm to solve this game. Results of small versions of the game are presented. Aleksander Sadikov and Ivan Bratko demonstrate Solving 20 £ 20 Puzzles. They use real-time A* (RTA*) to solve instances of this large version of the well-known sliding-tile puzzles in a reasonable amount of time. Their discovery is based on a recent ¯nding that RTA* works much better with strictly pessimistic heuristics. In Reflexive Monte-Carlo Search, Tristan Cazenave shows that the success of Monte-Carlo methods is not limited to Go, but also can be applied to Morpion Solitaire. Reflexive Monte-Carlo search for the non-touching version breaks the current record and establishes a new record of 78 moves. Another application of Monte Carlo outside the Go domain is described by Fran»coisVan Lishout, Guillaume Chaslot, and Jos W.H.M. Uiterwijk in their paper Monte-Carlo Tree Search in Backgammon. To their knowledge this is the ¯rst application of Monte-Carlo Tree Search to a 2-player game with chance. Preliminary experiments for Backgammon show that their method is suited for on-line learning for evaluating positions, contrary to the top-level Backgammon programs, that are based on o®-line learning. A third application of using Monte Carlo in other games is presented in The Monte-Carlo Approach in Amazons, by Julien Kloetzer, Hiroyuki Iida, and Bruno Bouzy. Since Amazons is a game that has the huge branching factor in common with Go, it seemed worthwhile to test Monte Carlo in this domain also. According to experiments the best way is to combine Monte Carlo with a good evaluation function to obtain a high-level program. Gaming Tools Besides research on speci¯c abstract computer games presented above, two groups of authors present work on games in a more general framework. Whereas there has been some work devoted to general gaming engines, these are mainly restricted to complete-information board games. In Extended General Gaming Model, Michel Quenault and Tristan Cazenave present an open func- tional model and its tested implementation. This model extends general gaming to more general games, both card games and board games, both complete- and incomplete-information games, both deterministic and chance games. Furthermore, Yngvi BjÄornssonand J¶onheidur ¶Isleifsd¶ottirintroduce in GTQL: A Query Language for Game Trees a language speci¯cally designed to query game trees. A software tool based on GTQL helps program developers to gain added insight into the search process, and makes regression testing easier. Ex- periments show that GTQL is both expressive and e±cient in processing large game trees. Video Games Whereas abstract games have been in the main stream of AI research already for many years, recently video games started to attract attention in an increasing rate. In the last part of this proceedings we present two papers presenting AI research in the domain of video games. In Predicting Success in an Imperfect-Information Game, Sander Bakkes, Pieter Spronck, Jaap van den Herik, and Philip Kerbusch present their ap- proach for creating an adaption mechanism for automatically transforming do- main knowledge into an evaluation function. Experiments are performed in the RTS game Spring using TD-learning.
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