Search Algorithms in AI Last Updated : 28 Jul, 2025 Comments Improve Suggest changes Like Article Like Report Search algorithms in AI help find solutions by exploring possible paths or options in a problem space. AI uses them in tasks like pathfinding, decision making and game playing. These algorithms work by searching through a set of possibilities to reach a goal, either blindly without extra information or with guidance using heuristics.Types of search algorithms Search Algorithms in AIThere are mainly 2 types of search algorithms i.e Uninformed Search Algorithms and Informed Search Algorithms.Uninformed Search AlgorithmsUninformed search also called blind search explores the search space without any domain specific knowledge or heuristics. It treats all nodes equally and chooses which path to explore next based solely on general rules like node depth or path cost. 1. Depth First SearchDepth First Search explores paths by going as deep as possible along one direction before backtracking. It uses a stack or recursion to keep track of the path.DFS is memory efficient compared to BFS since it doesn’t need to store all siblings at each level.However it is not guaranteed to find the shortest path and may get stuck in an infinite loop if the search tree is deep or contains cycles unless depth limits or visited checks are applied.For Example: Which solution would DFS find to move from node S to node G if run on the graph below.As DFS traverses the tree deepest node first it would always pick the deeper branch until it reaches the solution or it runs out of nodes and goes to the next branch.Path: S->A->B->C->G 2. Breadth First SearchBreadth First Search is a fundamental search algorithm that explores all possible paths level by level. It begins from the root node and explores all neighboring nodes before moving to the next level of nodes.BFS is complete and guarantees finding the shortest path if each move has the same cost.However its main drawback is high memory usage as it stores all nodes at the current level before moving deeper which can grow rapidly for large or complex problems.For Example: Which solution would BFS find to move from node S to node G if run on the graph below. As BFS traverses the tree shallowest node first it would always pick the shallower branch until it reaches the solution or it runs out of nodes and goes to the next branch.Path: S->D->G 3. Uniform Cost SearchUniform Cost Search is similar to BFS but takes the cost of each move into account. It always expands the node with the lowest cumulative path cost from the start.This makes UCS optimal and complete and useful when actions have different costs such as in navigation systems.It uses a priority queue to manage the frontier and ensures the cheapest path is always chosen next.For Example: Which solution would UCS find to move from node S to node G if run on the graph below.The cost of each node is the cumulative cost of reaching that node from the root and based on the UCS strategy the path with the least cumulative cost is chosen.Path: S->A->B->G Informed Search AlgorithmsInformed search uses domain knowledge in the form of heuristics to make smarter decisions during the search process. These heuristics estimate how close a state is to the goal guiding the search more efficiently.1. Greedy SearchGreedy search is a heuristic based algorithm that selects the path which appears to lead most directly toward the goal.It makes decisions based solely on the estimated cost from the current node to the goal (h(n)) ignoring the cost already taken to reach the current node (g(n)).This approach makes greedy search fast and memory efficient as it focuses only on immediate gains.For Example: Find the path from S to G using greedy search, the heuristic values h of each node below the name of the node. Starting from S we can traverse to A(h=9) or D(h=5). We choose D as it has the lower heuristic cost.Now from D we can move to B(h=4) or E(h=3). We choose E with a lower heuristic cost.Finally from E we go to G(h=0). This entire traversal is shown in the search tree below, in blue. Path: S->D->E->G 2. A* Tree Search:A* tree search also uses the f(n) = g(n) + h(n) evaluation but treats the search space as a tree which means it doesn’t track already visited nodes. Every path is explored independently even if it leads to the same state.This can result in duplicate work and a larger search space, especially in graphs with cycles.Although it’s simpler to implement A* tree search may be less efficient and is typically used when the search structure is naturally a tree or when memory constraints prevent maintaining a closed list.For Example: Find the path to reach from S to G using A* search. Starting from S the algorithm computes g(x) + h(x) for all nodes in the fringe at each step choosing the node with the lowest sum. The entire work is shown in the table below. It search like:Pathh(x)g(x)f(x)S707 S -> A9312S -> D ✔527 S -> D -> B ✔42 + 1 = 37S -> D -> E32 + 4 = 69 S -> D -> B -> C ✔23 + 2 = 57S -> D -> B -> E ✔33 + 1 = 47 S -> D -> B -> C -> G05 + 4 = 99S -> D -> B -> E -> G ✔ 04 + 3 = 77Path: S->D->B-> G-> E and Cost: 73. A* Graph searchA* graph search is an informed algorithm that finds the shortest path in a graph by considering both the cost to reach a node (g(n)) and the estimated cost to the goal (h(n)).It keeps track of visited nodes using a closed list to avoid revisiting them which makes it efficient and prevents cycles or redundant paths.This approach ensures optimality and completeness if the heuristic is admissible.It’s suitable for complex environments like road maps or game levels where paths may loop or intersect.For Example: Use graph searches to find paths from S to G in the following graph. In this Algo we keep a track of nodes explored so that we don't re explore them. Path: S->D->B->E->G and Cost: 7 Comparison of Different Search AlgorithmsAlgorithmTime ComplexitySpace ComplexityCompleteOptimalBreadth First SearchO(b^d)O(b^d)YesYes (if step cost is same)Depth First SearchO(b^d)O(d)NoNoUniform Cost SearchO(b^{1 + \frac{C^*}{\epsilon}})O(b^{1 + \frac{C^*}{\epsilon}})YesYesGreedy SearchO(b^m) O(b^m)NoNoA* Tree SearchO(b^d)O(b^d)Yes (if h is admissible)Yes (if h is admissible)A* Graph SearchO(b^d)O(b^d)Yes (if h is admissible)Yes (if h is admissible)Related Articles: Artificial Intelligence (AI) AlgorithmsInformed Search Algorithms in Artificial IntelligenceDifference between Informed and Uninformed Search in AIUninformed Search Algorithms in AI Comment More infoAdvertise with us M MdRafiAkhtar Follow Improve Article Tags : Technical Scripter Machine Learning Practice Tags : Machine Learning Similar Reads Machine Learning Tutorial Machine learning is a branch of Artificial Intelligence that focuses on developing models and algorithms that let computers learn from data without being explicitly programmed for every task. 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