Búsqueda informada Algoritmos are a class of algoritmos de búsqueda used in inteligencia artificial that leverage additional information about the problem domain to make the search process more efficient. Unlike uninformed search algorithms that explore the search space without any specific guidance, informed search algorithms use heuristics—rules or methods that help estimate the cost or distance to the goal. This additional information allows these algorithms to prioritize certain paths and make more strategic decisions during the search process.
One of the most well-known informed search algorithms is the A* (A-star) algorithm, which combines features of both uniform-cost search and pure búsqueda heurística. A* uses a función de costo that takes into account the cost to reach a node and an estimated cost to reach the goal from that node. By evaluating nodes based on this combined cost, A* effectively narrows down the search space and can often find the shortest path to the goal more quickly than uninformed methods.
Other examples include Greedy Best-First Search, which focuses solely on the heuristic estimate of the cost to reach the goal, and IDA* (Iterative Deepening A*), which combines the depth-first search strategy with the A* algorithm’s evaluación heurística. These informed search algorithms are widely used in various applications, including pathfinding in video games, robotics navigation, and route planning in transportation systems.