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Titlebook: Computers and Games; Second International Tony Marsland,Ian Frank Conference proceedings 2001 Springer-Verlag Berlin Heidelberg 2001 Comput

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A Least-Certainty Heuristic for Selective SearchWe present a new algorithm for selective search by iterative expansion of leaf nodes. The algorithm reasons with leaf evaluations in a way that leads to high confidence in the choice of move at the root. Performance of the algorithm is measured under a variety of conditions, as compared to minimax with ./. pruning, and to best-first minimax.
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Awari Retrograde AnalysisThis paper introduces an alternative algorithm for Retrograde Analysis in Awari. A parallel version of this algorithm created Awari endgame databases of up to 35 pebbles. Both sequential and parallel versions of the algorithm are compared to sequential and parallel versions of the traditional Retrograde Analysis algorithm.
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Lambda-Search in Game Trees — with Application to Goifferent orders of threat-sequences (so-called lambda-trees), focusing the search on threats and threat-aversions, but still guaranteeing to find the minimax value (provided that the game-rules allow passing or zugzwang is not a motive). Using negligible working memory in itself, the method seems ab
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Abstract Proof Searchausible moves in each position. As in early Chess programs, these methods have shortcomings, they often neglect good moves or overlook a refutation. We propose a safe method to select the interesting moves using game definition functions. This method has multiple advantages over basic alphabeta sear
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Strategies for the Automatic Construction of Opening Booksd moves suggested by theory, or simply by listing all games ever played by strong players [.,.,.]. Interest has recently shifted to automatic opening book construction where positions are selected by a best-first strategy, evaluated using a brute force search and then added to the opening book [.]..
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Construction of Chinese Chess Endgame Databases by Retrograde Analysisechnique in the construction of Chinese chess endgame databases. Although the two types of chess have the same number of pieces, similar individual characteristics for pieces, and comparable scales of the size of the boards, the fundamental differences in their playing rules lead to different constr
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Learning from Perfection to, are challenging but potentially very powerful tools. While some advances have been made in parameter tuning, the field of feature discovery is still in its infancy. The game ofAwari offers the possibility to achieve both goals. This paper describes the efforts to design an evaluation function w
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