Iterative Deepening é uma busca algorithm used in inteligência artificial that combines the advantages of both busca em profundidade and breadth-first search. It is particularly useful in scenarios where the depth of the solution is unknown or when memory recursos são limitados.
The algorithm works by repeatedly performing depth-first searches to a specified depth limit, incrementally increasing this limit with each iteration. Initially, it starts with a depth limit of zero, searches for solutions at that depth, and then increases the limit by one for the next search. This process continues until a solution is found or the maximum depth is reached.
Um dos principais benefícios do Deepening Iterativo é que ele usa menos memória em comparação com a busca em largura, pois precisa armazenar apenas um caminho do nó raiz até o nó atual e todos os nós irmãos nessa profundidade, em vez de armazenar todos os nós no nível atual. Isso o torna mais escalável para problemas com grandes espaços de busca.
Additionally, Iterative Deepening ensures that the algorithm is complete and optimal, meaning that if a solution exists, it will eventually be found, and it will do so in the shortest path possible. The trade-off is that it may involve repeated exploration of the same nodes, leading to higher time complexity in some cases. However, for many practical applications, the efficiency in memory usage and guaranteed completeness makes Iterative Deepening a favored choice.