Beam search is a heuristic search algorithm commonly used in the field of artificial intelligence for solving optimization and search problems. It is particularly effective in scenarios where exhaustive search methods are impractical due to the size of the search space.
In beam search, a fixed number of partial solutions, called beams, are maintained at each step of the search process. The algorithm starts with an initial solution and generates a set of successors by expanding the current partial solutions. The successors are evaluated based on a scoring function that estimates their potential to lead to an optimal solution. The top-k successors with the highest scores are selected as the new beams, while the rest are discarded.
The algorithm repeats this process iteratively until it reaches a termination condition, such as finding an optimal solution or reaching a maximum number of iterations. At the end, the best solution among the beams is returned as the final result.
Beam search is known for its ability to explore a relatively large portion of the search space while keeping the computation cost manageable. By focusing the search on the most promising partial solutions, beam search strikes a balance between exploration and exploitation, improving the chances of finding good solutions efficiently.
Beam search has been widely used in various applications, including natural language processing, machine translation, speech recognition, and image captioning, where finding an optimal solution among a large set of possibilities is crucial.
