Conspiracy numbers for min-max search
Artificial Intelligence
Artificial Intelligence - Special issue on computer chess
Studying overheads in massively parallel MIN/MAX-tree evaluation
SPAA '94 Proceedings of the sixth annual ACM symposium on Parallel algorithms and architectures
The Secret of Selective Game Tree Search, When Using Random-Error Evaluations
STACS '02 Proceedings of the 19th Annual Symposium on Theoretical Aspects of Computer Science
Parallel Controlled Conspiracy Number Search
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
Error analysis in minimax trees
Theoretical Computer Science - Algorithmic combinatorial game theory
An effective two-level proof-number search algorithm
Theoretical Computer Science - Algorithmic combinatorial game theory
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Tree search algorithms play an important role in many applications in the field of artificial intelligence. When playing board games like chess etc., computers use game tree search algorithms to evaluate a position. In this paper, we present a procedure that we call Parallel Controlled Conspiracy Number Search (Parallel CCNS). Shortly, we describe the principles of the sequential CCNS algorithm, which bases its approximation-results on irregular subtrees of the entire game tree. We have parallelized CCNS and implemented it in our chess program P. ConNerS, which now is the first in the world, that could win a high-ranked Grandmaster chess-tournament. We add experiments that show a speedup of about 50 on 159 processors running on an SCI-workstation-cluster.