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首页> 外文期刊>Journal of Economics and Sustainable Development >Adaptive Backtracking Search Strategy to Find Optimal Path for Artificial Intelligence Purposes
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Adaptive Backtracking Search Strategy to Find Optimal Path for Artificial Intelligence Purposes

机译:自适应回溯搜索策略以找到人工智能最佳路径

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摘要

There are numerous of Artificial Intelligence (AI) search strategies that used for finding the solution path to a specific problem, but many of them produce one solution path with no attention if it is the optimal path or not. The aim of our work is to achieve the optimality by finding direct path from the start node to the goal node such that it is the shortest path with minimum cost .In this paper adaptive backtracking algorithm is produced to find the optimal solution path, such that all possible paths in the tree graph of the search problem that have an expected optimal solution is tested, also a heuristic function related to the actual cost of the moving from one node to another is used in order to reduce the search computation time. The adaptive algorithm ignored any path that it is not useful in finding the optimal solution path, our adaptive algorithm implemented using visual prolog 5.1, evaluated on tree diagram and produced good result in finding the optimal solution path with efficient search time equivalent to O(bd/2) and space complexity O(bd). Keywords: Backtracking Algorithm, Optimal solution Path, Heuristic function, Dead end, shortest path, Minimum cost.
机译:有许多种人工智能(AI)搜索策略可用于找到特定问题的解决方案路径,但是无论是否为最佳路径,它们中的许多策略都不会引起人们的关注。我们的工作目的是通过找到从起始节点到目标节点的直接路径以使其为最短路径且成本最低来实现最优。本文提出了自适应回溯算法来找到最佳解路径,从而测试了搜索问题树图中具有预期最佳解决方案的所有可能路径,并且还使用了与从一个节点移动到另一个节点的实际成本相关的启发式函数,以减少搜索计算时间。自适应算法忽略了对找到最优解路径无用的任何路径,我们的自适应算法是使用Visual Prolog 5.1实现的,在树形图上进行了评估,并且在寻找最优解路径时取得了很好的结果,有效搜索时间等于O(bd / 2)和空间复杂度O(bd)。关键字:回溯算法,最优解路径,启发式函数,死角,最短路径,最小成本。

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