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Titlebook: Computers and Games; 4th International Co H. Jaap Herik,Yngvi Björnsson,Nathan S. Netanyahu Conference proceedings 2006 Springer-Verlag Ber

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*-M, Performance in Backgammon,p from 3) under tournament conditions on regular hardware without using risky forward-pruning techniques. We also present empirical evidence that with today’s sophisticated evaluation functions good checker play in backgammon does not require deep searches.
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,Associating Shallow and Selective Global Tree Search with Monte Carlo for 9 × 9 Go,ior to the other ones. At each iteration, random games are started at leaf nodes to compute mean values. The progressive pruning rule and the min-max rule are applied to non terminal nodes. We set up experiments demonstrating the relevance of this approach. . used this algorithm at the 8. Computer Olympiad held in Graz.
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Learning to Estimate Potential Territory in the Game of Go,ds. New trainable methods are presented for learning to estimate potential territory from examples. The trainable methods can be used in combination with our previously developed method for predicting life and death [25]. Experiments show that all methods are greatly improved by adding knowledge of life and death.
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Searching for Compound Goals Using Relevancy Zones in the Game of Go,. In this paper, we propose a general method that uses these relevancy zones for searching for compound goals. A compound goal is constructed from less complex atomic goals, using the standard connectives. In contrast to other approaches that treat goals separately in the search phase, compound goal search obtains exact results.
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https://doi.org/10.1007/978-0-387-72772-1ogram to play the opening well. We discuss a number of possible methods for creating opening books, focusing mainly on automatic construction, and explain which seem best suited for games with large branching factors such as Amazons.
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