AI Search Algorithms Interactive Demo

Educational Network Design

This simplified 5-city network provides 2 distinct paths from Riyadh to Makkah:

Direct Route: Riyadh → Jeddah → Makkah (929km, 2 hops)
Scenic Route: Riyadh → Qassim → Medina → Jeddah → Makkah (1,228km, 4 hops)

This design clearly demonstrates how different algorithms make different choices!

Breadth-First Search (BFS)

BFS explores all nodes at the current depth level before moving to nodes at the next depth level. It guarantees finding the shortest path in unweighted graphs.

Step Controls

Animation Speed:

Legend

Start/Path

Goal

Exploring

Explored

Frontier

Ready to Start

Select an algorithm above and click "Next Step" to begin the search, or click "Auto Play" for automatic visualization.

Current Status: Ready

Algorithm Comparison BFS Tutorial DFS Tutorial UCS Priority Queue UCS Dictionary State Space

Pseudocode

Data Structures

Frontier:

Empty

Explored:

Empty

State Graph

Tree visualization starting from Riyadh

Performance Metrics

Steps

Explored

Path Cost

Path Length

Algorithm Insights: Different Problems, Different Solutions

Is This the "Shortest Path Problem"?

Not exactly! Our demo shows different types of pathfinding problems. Only Dijkstra and A* solve the true "shortest path problem" (minimum distance). BFS and DFS solve different optimization problems entirely.

Problem Types Solved

Shortest Path Problem

Goal: Minimize total distance/cost

Algorithms: Dijkstra, A*

Our Result: Riyadh → Jeddah → Makkah (929km)

Fewest Hops Problem

Goal: Minimize number of intermediate stops

Algorithm: BFS

Our Result: Riyadh → Jeddah → Makkah (2 stops)

Any Path Problem

Goal: Find any route to destination

Algorithm: DFS

Our Result: Either route (depends on search order)

Our Network Results

Algorithm	Path Found	Distance	Hops
Dijkstra	Direct Route	929km	2
A*	Direct Route	929km	2
BFS	Direct Route	929km	2
DFS	Variable	Variable	2 or 4

Note: In our network, BFS happens to find the optimal distance, but this is coincidental. In larger networks, BFS often finds longer paths.

When to Use Each Algorithm

Use Dijkstra When:

You need the absolutely shortest distance
Graph has weighted edges (different costs)
Optimality is more important than speed
Example: GPS navigation, network routing

Use A* When:

You need shortest distance AND fast search
You have a good heuristic function
Graph is large (A* explores fewer nodes)
Example: Game pathfinding, robotics

Use BFS When:

You want fewest intermediate stops
Graph is unweighted (all edges equal cost)
Exploring by levels makes sense
Example: Social networks, minimum transfers

Use DFS When:

Any path is acceptable (not optimal)
You want to explore deeply first
Memory is limited (DFS uses less)
Example: Maze solving, decision trees

Key Educational Takeaways

Different algorithms solve different problems
BFS ≠ Shortest path (only shortest in unweighted graphs)
Dijkstra guarantees shortest distance
A* is Dijkstra with smarter search

DFS is fast but not optimal
Choose algorithm based on your goal
Optimality often comes with computational cost
Real-world applications vary by use case