Analyzing uninformed search strategy algorithms in state space search

Search plays a vital role in tackling many problems in artificial intelligence. Search can be considered as a universal problem solving mechanism in Artificial Intelligence world. Search is most essential as no models in the world are complete, computable and consistent. Solutions to the problem cannot be precomputed and many problems have to be tackled dynamically by considering the observed data. Search framework in Artificial Intelligence can be categorized into state space search, problem reduction search and game tree search. This paper concentrates on one of the aspect of state space search i.e., uninformed search. Uninformed search will be deprived of domain specific information. Search algorithm has to run without any additional knowledge. Different algorithms of uninformed search will be analyzed against time, memory, completeness and optimality. These algorithms are put up in the tabular form, compared and contrasted along with merits and demerits which will enable to pick an appropriate algorithm for a unique problem definition with the memory and time constraints.