Fuzzified tree search in real domain games

Authors:
Dmitrijs Rutko
Affiliations:
Faculty of Computing, University of Latvia, Riga, Latvia
Venue:
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Year:
2011

Citing 5
Cited 1

Best-first fixed-depth minimax algorithms

Artificial Intelligence
Parallel Search of Strongly Ordered Game Trees

ACM Computing Surveys (CSUR)
The solution for the branching factor of the alpha-beta pruning algorithm and its optimality

Communications of the ACM
Selective depth-first game-tree search

Selective depth-first game-tree search
Artificial Intelligence: A Modern Approach

Artificial Intelligence: A Modern Approach

Fuzzified game tree search: precision vs speed

PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence

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Abstract

Fuzzified game tree search algorithm 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. Applied to an abstract domain, it outperforms the existing ones such as Alpha-Beta, PVS, Negascout, NegaC*, SSS*/ Dual* and MTD(f). In this paper we present experimental results in real domain games, where the proposed algorithm demonstrated 10 percent performance increase over the existing algorithms.