Fuzzified Algorithm for Game Tree Search with Statistical and Analytical Evaluation
Dmitrijs Rutko · 2011
This paper presents a new game tree search algorithm which is based on the idea that the exact game tree evaluation is not required to find the best move. Therefore, pruning techniques may be applied earlier resulting in faster search and greater performance. The experiments show that applied to an abstract domain, the presented algorithm outperforms the existing ones such as PVS, Negascout, NegaC*, SSS*/ Dual* and MTD(f). This paper also provides improvements for algorithm such as statistical and analytical game tree evaluation.