Search Algorithms in Artificial Intelligence: Informed vs Uninformed Search

Best first search in artificial intelligence is an informed search algorithm that expands the node with the lowest heuristic value h(n) — the node that appears closest to the goal. Unlike uninformed methods, it skips irrelevant paths. It is fast but neither complete nor guaranteed to find the optimal solution.

• Uninformed search (BFS, DFS, uniform cost, iterative deepening) has no knowledge of the goal’s location and explores systematically.
• Informed search (best first, A*) uses a heuristic to guide expansion toward the goal more efficiently.
• A* search combines actual path cost and heuristic estimate, giving the best balance of speed and optimality.
• Alpha-beta pruning cuts branches in a minimax game tree that cannot affect the final decision, speeding up adversarial search.
• Monte Carlo tree search uses random simulation to evaluate moves, powering modern game-playing AI like AlphaGo.

Uninformed Search Strategies and Their Trade-offs

Uninformed search, sometimes called blind search, knows only the problem definition. It has no idea how far it is from the goal. Every node is treated equally until the goal is actually found. That simplicity is both its strength and its weakness.

To keep every algorithm comparable, imagine a small maze with a start node S and a goal node G. Nodes are A, B, C, D, E between them. Each edge has a cost. We will use this same graph throughout the article.

Breadth First Search (BFS)

Breadth first search expands all nodes at the current depth before moving deeper. In our maze, it checks every neighbor of S before checking neighbors of those neighbors. It is complete (it will find a solution if one exists) and optimal when all edge costs are equal. The problem is memory: BFS stores every node at the current frontier, which grows exponentially. For a branching factor of 10 at depth 6, that is 10⁶ nodes in memory simultaneously.

Depth First Search (DFS)

Depth first search dives down one branch as far as possible before backtracking. In the maze, it would go S-A-C-G if that path exists, ignoring B and D until forced to backtrack. DFS uses far less memory (linear in depth) but it is not complete in infinite spaces and it is not optimal. It can get stuck chasing a very long wrong path before finding the goal.

The difference between DFS and BFS in AI comes down to this: BFS guarantees the shortest path in terms of steps; DFS uses minimal memory but can miss shorter paths entirely. For GATE AI exam questions, remember BFS is optimal with uniform costs, DFS is not.

Uniform Cost Search

Uniform cost search in artificial intelligence is BFS’s smarter sibling. Instead of expanding the shallowest node, it expands the node with the lowest cumulative path cost from the start. In our maze, if the path S-B-G costs 3 and S-A-C-G costs 5, uniform cost search finds S-B-G first. It is complete and optimal, but it expands outward in rings of equal cost, which can be slow when the goal is far away.

Iterative Deepening Search

Iterative deepening in artificial intelligence combines the memory efficiency of DFS with the completeness of BFS. It runs DFS repeatedly, each time with a deeper depth limit (1, then 2, then 3, and so on). Nodes near the top get re-expanded multiple times, but because most nodes are at the deepest level, the overhead is surprisingly small. It is the preferred uninformed method when search depth is unknown, and it is both complete and optimal for uniform step costs.

Informed Search and the Role of Heuristics

Informed search, also called heuristic search, gives the algorithm extra information: an estimate of how far any given node is from the goal. This estimate is the heuristic function h(n). A good heuristic does not overestimate the true cost (that is the admissibility condition), and it is consistent if h(n) never exceeds the cost of moving to a neighbor plus that neighbor’s heuristic value.

Heuristic search techniques in artificial intelligence are what separate academic toy problems from real-world AI systems. Route planning apps like Google Maps and Ola in India, robotics path-planning, and NLP parsing all rely on heuristics to make search tractable at scale. According to Helmert and Röger’s 2023 survey in the Journal of Artificial Intelligence Research (Vol. 76), A*-based heuristic search remains the most widely deployed exact path-finding method in robotics and game AI.

Best First Search in Artificial Intelligence

Best first search in artificial intelligence expands the node that appears closest to the goal according to h(n) alone. In our maze, if node B has h(B) = 2 and node A has h(A) = 5, best first search expands B next regardless of how much it cost to reach B. It is fast and memory-light compared to BFS, but it is neither complete (it can get trapped in loops without cycle detection) nor optimal (a low heuristic value does not mean the total path cost is low).

Think of best first search as a greedy algorithm: it always chases what looks best right now. That works brilliantly when the heuristic is accurate, and fails when the heuristic misleads. The GATE 2024 AI paper included a question on identifying which property best first search violates — optimality is the correct answer.

A* Search Algorithm

The A* algorithm fixes best first search’s optimality problem by evaluating nodes using f(n) = g(n) + h(n), where g(n) is the actual cost from the start to node n and h(n) is the heuristic estimate to the goal. In our maze, even if B looks close to the goal, A* will not ignore a cheap path through C that actually costs less overall.

A* is complete and optimal when h(n) is admissible. It is the gold standard for single-agent pathfinding and is used in GPS navigation, Swiggy delivery routing, video game NPC movement, and robotic motion planning. IIT Bombay’s AI curriculum uses A* as the central case study for comparing search complexity because it elegantly bridges the informed-versus-uninformed gap. NPTEL’s AI course (CS 6300) also dedicates a full module to A* and its variants.

If you are preparing for placements or GATE, understanding A* is non-negotiable. You can explore structured preparation through online certification courses that cover AI fundamentals with hands-on labs.

Algorithm Comparison Table

Algorithm	Type	Complete?	Optimal?	Time Complexity	Space Complexity
BFS	Uninformed	Yes	Yes (uniform cost)	O(bd)	O(bd)
DFS	Uninformed	No (infinite spaces)	No	O(bm)	O(bm)
Uniform Cost Search	Uninformed	Yes	Yes	O(b1+C*/ε)	O(b1+C*/ε)
Iterative Deepening	Uninformed	Yes	Yes (uniform step)	O(bd)	O(bd)
Best First Search	Informed	No (without detection)	No	O(bm)	O(bm)
A* Search	Informed	Yes	Yes (admissible h)	O(bd)	O(bd)

b = branching factor, d = depth of shallowest solution, m = maximum depth, C* = optimal cost, ε = minimum edge cost. Source: Russell & Norvig, Artificial Intelligence: A Modern Approach, 4th edition.

Adversarial Search: Minimax, Pruning and MCTS

When two players compete, the search problem changes fundamentally. You are no longer just finding a path to a goal. You are finding the best move assuming your opponent plays perfectly against you. That is the domain of adversarial search.

Game-playing AI has become one of the most visible benchmarks for AI progress, from IBM Deep Blue’s 1997 chess victory to AlphaGo’s 2016 defeat of Lee Sedol. These systems all depend on adversarial search at their core.

Minimax Algorithm

The minimax algorithm builds a game tree where the MAX player tries to maximise their score and the MIN player tries to minimise it. The algorithm recursively assigns values to terminal nodes (win, loss, draw) and backs those values up the tree. At MAX nodes, pick the child with the highest value. At MIN nodes, pick the child with the lowest. The result is the optimal move assuming both players are rational.

The problem is exponential cost. Chess has a branching factor of about 35 and games last around 80 moves, giving roughly 35⁸⁰ nodes. That is not searchable in any reasonable time without pruning.

What is Alpha-Beta Pruning?

Alpha-beta pruning solves the minimax explosion by cutting branches that cannot possibly affect the final decision. It maintains two values: alpha (the best MAX has found so far) and beta (the best MIN has found so far). When beta is less than or equal to alpha at any node, that subtree is pruned because the opponent would never allow the game to reach it.

Here is a worked example on our game tree. Suppose MAX is evaluating two moves. The first move leads to values [3, 5, 2] at leaf nodes, so MAX knows it can get at least 3 from that branch. Now MAX evaluates the second move and finds the first leaf is 1. MIN would choose 1 or lower from this branch, which is already worse than 3. So MAX prunes all remaining children of the second move. No need to evaluate them. The answer is already determined.

In the best case (perfect move ordering), alpha-beta pruning reduces the effective branching factor from b to roughly the square root of b, meaning you can search twice as deep in the same time. That is why it is used in every serious chess and checkers engine. If you want to understand what is alpha beta pruning in artificial intelligence beyond the textbook definition, think of it as teaching the algorithm to stop reading a book once the ending is obvious.

Monte Carlo Tree Search (MCTS)

Monte Carlo tree search takes a completely different approach. Instead of evaluating positions with a heuristic function, it plays out thousands of random simulated games from each candidate move and averages the results. Moves that win more random simulations get higher scores. MCTS has four phases: selection (pick a promising node using UCB1), expansion (add a new child), simulation (play randomly to terminal), and backpropagation (update scores up the tree).

MCTS is why AlphaGo defeated world champions. Go has a branching factor of around 250, making minimax completely intractable. MCTS, combined with deep neural networks for position evaluation, made superhuman Go play possible. According to Silver et al. in Nature (Vol. 529, 2016, doi:10.1038/nature16961), AlphaGo’s MCTS-based system won 99.8% of games against other Go programs before facing human professionals.

MCTS is also used in real-time strategy games, medical decision support, and automated theorem proving. It is one of the most practically impactful search algorithms developed in the last two decades. You can read more about how AI is reshaping careers and industries on the 3.0 University blog.

Understanding these techniques is genuinely useful beyond academics. If you are thinking about how to future-proof your career in the age of AI, search algorithms are foundational knowledge that appears in ML engineering interviews, AI research roles, and product teams building recommendation systems. Indian companies like Zomato, Ola, and Flipkart use A*-based and heuristic search variants in their logistics and delivery routing systems.

Putting It All Together: Next Steps This Week

Start by implementing BFS and DFS on a simple graph in Python. Once those feel comfortable, add a heuristic and convert your BFS into A*. Trace through the alpha-beta pruning example on paper with a tic-tac-toe tree before touching code. That sequence, theory then trace then code, is how search algorithms actually stick.

The Big Data Analytics notes on 3.0 University connect search and optimization concepts to large-scale data systems, which is a natural next topic once you are comfortable with graph traversal. For peer discussion and doubt-solving, the REACH learner community has active threads on AI and data science topics where you can post your implementations and get feedback.

If you want structured, mentor-guided learning rather than self-study, the bootcamp training programs at 3.0 University cover AI, cybersecurity, and programming with live projects and placement support.

Frequently Asked Questions

What is best first search in artificial intelligence?

Best first search is an informed search algorithm that expands the node with the lowest heuristic value h(n), meaning the node that appears closest to the goal. It is faster than uninformed methods but is not guaranteed to find the optimal path. It works well when the heuristic is accurate and the search space does not have misleading dead ends.

What is the difference between informed and uninformed search in artificial intelligence?

Uninformed search (BFS, DFS, uniform cost, iterative deepening) has no knowledge about the goal’s location and explores based purely on the problem structure. Informed search (best first, A*) uses a heuristic function to estimate the distance to the goal, allowing it to prioritise promising paths and reach solutions faster with fewer node expansions on average.

What is alpha-beta pruning?

Alpha-beta pruning is an optimisation for the minimax algorithm used in two-player game AI. It eliminates branches of the game tree that cannot influence the final decision by tracking the best values found for each player. In ideal conditions it halves the effective search depth required, making adversarial search practical for complex games like chess.

What is the difference between DFS and BFS in AI?

BFS explores all nodes at the current depth level before going deeper, guaranteeing the shortest path but using exponential memory. DFS dives deep along one branch before backtracking, using linear memory but missing shorter paths and potentially looping infinitely. BFS is optimal with uniform step costs; DFS is not optimal and not complete in infinite search spaces.

What is uniform cost search in artificial intelligence?

Uniform cost search expands the node with the lowest cumulative path cost from the start rather than the shallowest node. It is complete and optimal for any non-negative edge costs. It is slower than informed methods because it explores in rings of equal cost without any knowledge of the goal’s direction, but it guarantees the cheapest solution.

What is iterative deepening search?

Iterative deepening search runs depth-first search repeatedly with increasing depth limits (1, 2, 3, and so on). It combines DFS’s low memory usage with BFS’s completeness and optimality for uniform step costs. It is the preferred uninformed strategy when the solution depth is unknown, and it appears regularly in GATE AI syllabus questions on search complexity.

What is Monte Carlo tree search?

Monte Carlo tree search (MCTS) evaluates game moves by running thousands of random simulated play-outs and averaging outcomes. It selects, expands, simulates, and backpropagates scores repeatedly. It is especially effective in games with huge branching factors like Go, where traditional minimax is computationally impossible. DeepMind’s AlphaGo used MCTS combined with neural networks to achieve superhuman performance.

Search algorithms are one of those topics that reward the student who takes them seriously early. Every ML interview, every AI systems design question, every robotics project eventually circles back to how you traverse a state space efficiently. Get these right and you will find the rest of AI theory clicks faster.

3.0 University’s AI certification courses in Artificial Intelligence, Cybersecurity, Ethical Hacking, Blockchain, and Web3 are built for students, fresh graduates, working professionals, and career switchers who want practical, industry-ready skills through hands-on labs and real-world projects, not just theory. If that is the kind of learning you are after, start there.

Last updated: June 2025. Reviewed by the 3University editorial team.