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Procedures for exploring state spaces to find solutions or paths.
DESCRIPTION
Search algorithms explore problem spaces to locate solutions, paths, or optimal configurations. Uninformed methods like BFS and DFS traverse blindly; informed methods like A* use heuristics for efficiency. In planning and routing, search guarantees feasibility and optimality under conditions. In sequence generation, beam search trades optimality for tractability. Practical implementations balance memory, time, and heuristic quality. Correctness and admissibility properties guide algorithm choice and tuning.
ALTERNATIVE DEFINITIONS
Wikipedia
In computer science, a search algorithm is any algorithm which solves the search problem, namely to retrieve information stored within some data structure, or calculated in the search space of a problem domain.
Frames search as retrieving or computing solutions in a search space.
Artificial Intelligence: A Modern Approach
Search algorithms systematically explore states generated by successor functions to find paths from initial states to goal states.
Emphasizes state-space exploration driven by successor functions.
RELATED TERMS
Planning Breadth-First Search Depth-First Search A-star Search Dijkstra’s Algorithm Heuristic Graph Optimization State Space Beam Search
COMPETING TERMS
Optimization Algorithm (Gradient-based)
RELATIONS
- Broader: Algorithm
KNOWLEDGE AREAS
AI Mathematics Data Science Software Engineering
TARGET AUDIENCE
Data Scientist AI Engineer Software Developer
EXTERNAL SOURCES
- Wikipedia - Search algorithm
- CP-Algorithms - Detailed BFS explanation
- Stanford - A* search tutorial and heuristics
- arXiv - Survey on heuristic search
TOP QUESTIONS
What is a Search Algorithm?
It’s a method to explore a state or data space to find a solution, path, or item of interest.
Category: Definition
When should I use informed search?
Use informed search when a good heuristic exists; it often reduces exploration and runtime compared to uninformed methods.
Category: How-to
What is the role of heuristics?
Heuristics estimate remaining cost and guide exploration. Admissible heuristics preserve optimality guarantees in algorithms like A*.
Category: Concept
What are common mistakes with Search Algorithms?
Using inadmissible heuristics without acknowledging suboptimality, ignoring memory limits, or failing to detect cycles.
Category: Mistakes
How is beam search different?
Beam search keeps only a fixed number of best candidates per step, trading completeness for speed.
Category: Comparison
What metrics matter for search?
Time, memory, solution quality, and branching factor dominate performance. Heuristic accuracy drives efficiency.
Category: Evaluation
How does search relate to planning?
Planning defines the problem and transitions; search finds the plan by exploring the implied state space.
Category: Relationship
Where to start learning search?
Study BFS/DFS, uniform-cost, and A* from AIMA or Stanford tutorials, then dive into domain-specific variants.
Category: Learning
Is brute-force search ever useful?
Yes, as a baseline or when the state space is tiny. It provides correctness checks for more complex methods.
Category: Opinion