Comparing search algorithms in used artificial intelligence applications

Search implementation is an important part of artificial intelligence. The most suitable solution must be found and searching is essential for solution of problem. Typically, Search algorithms are separated two importants groups. These are uniformed search and informed search. Uninformed search is also called blind search. This is because, Uninformed doesn`t use any information that about the problem. These algorithms are Breadth-first search, Depth-first search and Bidirectional (BF) Search. Informed Algorithms are more succesfully than uninformed search in search implementation. This is because, informed algorithms use some information that about the problem. Informed search is also called heuristic search. These algoritms are Best-first ? Greedy search, A* search. 8-puzzle problem have been used for compare search algorithms. For this process 1000 8-puzzle start state samples have been generated by using complete random and logical random methods. This generated samples have been tried to solve by BFS, DFS and A* algorithms and the expanded total state numbers for solution have been saved into text file. The results that were obtained have been undergone of statistical analysis with one-way variance analysis tecnique. Tukay test have been used to determine different groups. Z test have been used to compare of proportions. This study`s main aim is that explore and compare the effectiveness of well-known search algorithms, BFS, DFS and A* algorithms.