Introduction
Uninformed search algorithms explore a problem space without using domain knowledge.
They depend only on the problem structure and goal test. Each algorithm follows a different expansion strategy.
Breadth-First Search (BFS)
Breadth-First Search explores nodes level by level.
It expands all nodes at one depth before moving deeper.
The shallowest nodes are always explored first.
Advantages of BFS
BFS is complete.
It always finds a solution if one exists.
It finds the shortest path when all costs are equal.
Its behavior is predictable and systematic.
Limitations of BFS
Memory usage is very high.
All nodes at a level must be stored.
It becomes impractical for deep search spaces.
Time complexity increases rapidly with branching factor.
Depth-First Search (DFS)
Depth-First Search explores one branch deeply.
It backtracks only when no further expansion is possible.
It uses a stack-based approach.
Advantages of DFS
DFS uses very little memory.
It is easy to implement.
It can reach deep solutions quickly.
Limitations of DFS
DFS is not complete in infinite spaces.
It may get stuck in infinite paths.
It does not guarantee the shortest solution.
Solution quality depends on traversal order.
Uniform Cost Search (UCS)
Uniform Cost Search expands the lowest-cost path first.
It selects nodes based on cumulative path cost.
A priority queue controls expansion order.
Advantages of UCS
UCS always finds the optimal solution.
It works well with variable path costs.
It is complete for all positive step costs.
Limitations of UCS
UCS is slow for large search spaces.
Memory consumption is high.
Implementation is more complex.
Performance degrades when many paths have similar costs.
Depth-Limited Search (DLS)
Depth-Limited Search is a modified DFS.
It introduces a predefined depth limit.
Nodes beyond this limit are not expanded.
Advantages of DLS
DLS avoids infinite depth traversal.
Memory usage remains low.
It gives control over search depth.
It is efficient when solution depth is known.
Limitations of DLS
DLS is not complete if the limit is too small.
It may miss valid solutions.
It does not guarantee optimality.
Choosing the correct depth limit is difficult.
Comparative Insight
BFS prioritizes completeness.
DFS prioritizes memory efficiency.
UCS prioritizes cost optimality.
DLS prioritizes controlled exploration.
These algorithms represent different design trade-offs.
Understanding them is essential for effective AI problem solving.
Comparison of Common Uninformed Search Algorithms
| Feature | Breadth-First Search (BFS) | Depth-First Search (DFS) | Uniform Cost Search (UCS) | Depth-Limited Search (DLS) |
| Search strategy | Explores level by level | Explores one branch deeply | Expands lowest-cost path | DFS with a depth limit |
| Uses domain knowledge | No | No | No | No |
| Data structure used | Queue | Stack / Recursion | Priority queue | Stack with depth counter |
| Completeness | Yes | No (in infinite spaces) | Yes | Only if solution depth ≤ limit |
| Optimality | Yes (equal cost only) | No | Yes | No |
| Memory usage | Very high | Very low | High | Low |
| Time efficiency | Slow for large depth | Can be fast or slow | Slow due to cost comparison | Faster than DFS in deep spaces |
| Risk of infinite loops | No | High | No | Low |
| Control over depth | No | No | No | Yes |
| Best suited for | Shallow solutions | Memory-limited problems | Variable-cost problems | Known or estimated depth |
| Main limitation | Memory explosion | May never find solution | High time and memory cost | Misses deep solutions |
Key Insight
Each algorithm solves a different problem type.
No single method is best for all cases.
Depth-Limited Search offers a practical balance.
It controls infinite depth while preserving low memory use.
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