Abstract
Three parallel search strategies in bivalued game trees are described and compared in this paper. Two of them, the Top-Down and Bottom-Up Strategies, distribute available processors statically in the upper and lower levels of the game tree, respectively. The third strategy is more dynamic and needs information at the inner nodes of the search tree to decide whether the processors should be distributed at one node. For these search strategies formulas for the behavior of the average run time are developed, on the basis of a general probabilistic game tree model. The theoretical results obtained can be used to analyze the performance of the parallel search strategies before implementing them on a parallel computer. The three investigated parallel search strategies are tested empirically for a real game on a Transputer network. The empirical results match the theoretical results, indicating that the third strategy is best and the top-down strategy is worst.
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