Search results for "Depth-first search"
showing 3 items of 3 documents
Quantum algorithm for tree size estimation, with applications to backtracking and 2-player games
2017
We study quantum algorithms on search trees of unknown structure, in a model where the tree can be discovered by local exploration. That is, we are given the root of the tree and access to a black box which, given a vertex $v$, outputs the children of $v$. We construct a quantum algorithm which, given such access to a search tree of depth at most $n$, estimates the size of the tree $T$ within a factor of $1\pm \delta$ in $\tilde{O}(\sqrt{nT})$ steps. More generally, the same algorithm can be used to estimate size of directed acyclic graphs (DAGs) in a similar model. We then show two applications of this result: a) We show how to transform a classical backtracking search algorithm which exam…
Fuzzified Game Tree Search – Precision vs Speed
2012
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% (pr…
Fuzzified Tree Search in Real Domain Games
2011
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.