Informed Search
Informed search is a problem-solving technique in artificial intelligence and computer science that uses domain-specific knowledge or heuristics to guide the search process toward a goal more efficiently than uninformed (blind) search methods. It involves algorithms that evaluate the cost or potential of different paths using heuristic functions, which estimate the distance to the goal, to prioritize exploration and reduce the search space. Common examples include A* search, greedy best-first search, and hill climbing, which are widely applied in pathfinding, puzzle-solving, and optimization tasks.
Developers should learn informed search when working on AI-driven applications, game development, robotics, or any domain requiring efficient pathfinding or optimization, as it significantly improves performance by avoiding exhaustive exploration. It is particularly useful in scenarios with large state spaces, such as route planning in maps, solving puzzles like the 8-puzzle, or scheduling problems, where heuristic guidance can lead to faster and more optimal solutions compared to brute-force methods.