Imagine you're dropped into a massive corn maze. No map, no aerial view—just towering walls and branching paths in every direction. How do you find your way out without wandering in circles forever?

This is exactly the problem computers face constantly. Finding routes on maps, navigating game characters, even figuring out the best sequence of moves in a puzzle—all require pathfinding algorithms. These are systematic strategies for exploring possibilities and finding the best way through. The good news? The same intuitions that help you escape a corn maze can help you understand how computers solve much harder problems.

When you enter a maze, you have a choice. You could explore one path as far as it goes before trying another—diving deep into each possibility. Or you could check every path one step at a time, gradually expanding outward like ripples in a pond. These two approaches have names: depth-first search and breadth-first search.

Depth-first is like following your nose. Pick a direction, keep going until you hit a wall, then backtrack and try something else. It's memory-efficient because you only need to remember the path you're currently on. But it might lead you down a very long dead end while the exit was just around the first corner.

Breadth-first is more methodical. You check every possibility one step away, then every possibility two steps away, and so on. This guarantees you'll find the shortest path first—but it requires remembering all the partial paths you're still exploring. The strategy you choose depends on what matters more: finding any solution quickly, or finding the best solution.

Takeaway

Every search involves a tradeoff between going deep (committing to one path) and going wide (keeping options open). Knowing which matters more shapes your entire approach.

Here's a crucial maze skill: recognizing when you've made a wrong turn before you've wasted too much time. In a physical maze, you hit a wall and know to backtrack. In computational problems, dead ends are less obvious—but equally important to detect.

This is where backtracking comes in. You try a choice, see where it leads, and if it fails, you undo that choice and try something different. The key insight is keeping track of your decisions so you can systematically reverse them. Think of leaving breadcrumbs, but ones you can pick back up.

Smart pathfinding goes further with pruning—cutting off exploration paths early when you can prove they won't lead anywhere useful. If you know the exit is north and a path heads steadily south, why keep following it? Recognizing these hopeless situations early saves enormous amounts of wasted exploration.

Takeaway

The fastest way through a problem isn't always forward. Learning when to abandon a failing approach—and having a system for doing so cleanly—is often more valuable than persistence.

Sometimes any solution will do. But often, you need the best solution—the shortest route, the cheapest option, the fastest sequence. This is where pathfinding gets interesting.

The classic approach is Dijkstra's algorithm, which explores outward from your starting point, always expanding to the nearest unvisited location. It's like inflating a balloon from the start point—it touches closer things first. This guarantees you find the shortest path to everything, but it explores in all directions equally, even ones leading away from your goal.

A smarter variant is A* (A-star), which adds a twist: it estimates how far each partial path is from the goal and prioritizes paths that seem promising. Imagine you're in a maze but you can hear music from the exit—you'd naturally favor paths where the music gets louder. A* uses mathematical estimates the same way, focusing exploration toward likely solutions while still guaranteeing it finds the optimal answer.

Takeaway

Finding the best solution among many valid options requires balancing two things: certainty about what you've already explored, and intelligent guessing about where to look next.

Pathfinding algorithms aren't just about mazes and maps. They're templates for thinking about any problem where you need to search through possibilities systematically. Whether you're debugging code, planning a project, or solving a puzzle, the same principles apply.

Start with a clear exploration strategy. Recognize dead ends quickly and backtrack cleanly. And when you need the best answer—not just any answer—use what you know to guide your search toward promising territories. These patterns will serve you far beyond any single algorithm.