Best-first fixed-depth minimax algorithms
Artificial Intelligence
The solution for the branching factor of the alpha-beta pruning algorithm and its optimality
Communications of the ACM
Multi-cut &agr;&bgr;-pruning in game-tree search
Theoretical Computer Science
Selective depth-first game-tree search
Selective depth-first game-tree search
RankCut: a domain independent forward pruning method for games
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Fuzzified tree search in real domain games
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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Most game tree search algorithms consider finding the optimal move. That is, given an evaluation function they guarantee that selected move will be the best according to it. However, in practice most evaluation functions are themselves approximations and cannot be considered "optimal". Besides, we might be satisfied with nearly optimal solution if it gives us a considerable performance improvement. In this paper we present the approximation based implementations of the fuzzified game tree search algorithm. The paradigm of the algorithm allows us to efficiently find nearly optimal solutions so we can choose the "target quality" of the search with arbitrary precision --- either it is 100% (providing the optimal move), or selecting a move which is superior to 95% of the solution space, or any other specified value. Our results show that in games this kind of approximation could be an acceptable tradeoff. For example, while keeping error rate below 2%, the algorithm achieved over 30% speed improvement, which potentially gives us the possibility to search deeper over the same time period and therefore make our search smarter. Experiments also demonstrated 15% speed improvement without significantly affecting the overall playing strength of the algorithm.