How Pathfinding Algorithms Think | BFS, DFS, Dijkstra & A* Explained
Same maze. Same goal. Different algorithms.
Pathfinding is the science of deciding where to look next. A computer does not magically see the best route. It tests possible places, remembers what it has learned, avoids walls, compares costs, and uses rules to choose the next step.
In this video, we explain pathfinding algorithms visually: search graphs, nodes, edges, walls, frontier cells, visited cells, Breadth-First Search, Depth-First Search, Dijkstra’s Algorithm, heuristics, A*, dynamic obstacles, parent links, and final path reconstruction.
You’ll see why BFS expands like a wave, why DFS dives deep, why Dijkstra follows the cheapest known cost, and why A* combines cost so far with a smart guess toward the goal.
Chapters:
0:00 Same maze, different brains
0:17 How computers search
0:33 Turning the world into a graph
0:59 Frontier and visited cells
1:29 Breadth-First Search
2:04 Depth-First Search
2:36 Cost-based maps
3:04 Dijkstra’s Algorithm
3:34 Heuristics
4:05 A* Algorithm
4:38 When the world changes
5:06 Parent links and final path
5:41 BFS, DFS, Dijkstra and A* recap
Main ideas:
A maze can be represented as a graph
Walkable cells are nodes
Legal moves are edges
Walls block nodes or edges
The frontier is the living edge of the search
BFS expands evenly and can find the shortest path in equal-cost grids
DFS dives deeply but does not guarantee the shortest path
Dijkstra finds the cheapest path when movement costs differ
A* uses both real cost and a heuristic guess
Parent links let the algorithm reconstruct the final route
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