Abstract
A comprehensive analysis of state space search algorithms widely used in artificial intelligence (AI) problems has been carried out. Various classes of algorithms: blind exhaustive and limited search methods, heuristic algorithms, metaheuristic approaches, as well as their hybrid combinations are considered. A comparison was made based on key criteria: search strategy, systematicity, optimality, computational complexity, scalability, parallelizability, etc. Areas of effective application of each type of algorithm are identified. Recommendations for choosing an appropriate strategy depending on the characteristics of the problem are formulated. Promising directions for the development of methods based on hybridization, as well as machine learning technologies for adaptive optimization of search parameters, are considered. Keywords artificial intelligence, state space, search algorithms, blind search, heuristic search, metaheuristics, hybrid algorithm, machine learning
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