Heuristic Search
Heuristic search is an artificial intelligence and computer science technique that uses problem-specific knowledge (heuristics) to guide the exploration of a search space more efficiently than uninformed methods like breadth-first or depth-first search. It involves evaluating potential paths or states using a heuristic function that estimates the cost or distance to a goal, enabling algorithms to prioritize promising options and find solutions faster, often in complex domains like pathfinding, puzzle-solving, or optimization. Common algorithms include A*, greedy best-first search, and hill climbing, which balance exploration and exploitation based on heuristic estimates.
Developers should learn heuristic search when working on problems with large or infinite search spaces where brute-force methods are computationally infeasible, such as in game AI (e.g., chess or video game pathfinding), robotics navigation, scheduling, or data mining tasks. It is particularly valuable in AI applications, optimization problems, and any scenario requiring efficient decision-making under constraints, as it reduces search time and resource usage by leveraging domain-specific insights to approximate optimal solutions.