What information entered the process?
Labs / Computer Science
Pathfinding
Drag to draw and erase walls, then move the Start and Goal anywhere you like. Pick an algorithm and press play: visited cells fill in, the orange frontier shows what's next to explore, and the finished shortest path snaps into place.
Labs / Computer Science / Pathfinding
Pathfinding
Drag to draw and erase walls, then move the Start and Goal anywhere you like. Pick an algorithm and press play: visited cells fill in, the orange frontier shows what's next to explore, and the finished shortest path snaps into place.
Why it matters
Why explore this?
Computer science turns clear rules into reliable processes. This model lets you step through those rules and see how each decision changes the result.
Explore
Try the model
explored 0path —
Investigate
Questions to test
Predict first, try it in the model, then use what you observe to answer.
What to try
- Run BFS and then A* on the same maze — how many fewer cells does A* explore to find the exact same path?
- BFS and Dijkstra explore almost identically here. Why do they behave the same when every step costs one?
- Box the goal in completely with walls. What does the search do when no path exists?
- Drag the goal right next to a wall maze. Can you build a layout where A* gets "fooled" and explores a dead end?
- Slow the speed all the way down. Which direction does the frontier grow — a spreading circle, or a beam aimed at the goal?
Explain
Make sense of what happened
Trace the process one step at a time. Name the rule used at each important step, then explain why the final result follows from those rules.
Check
Before you leave
Which rule changed it?
Why did the process produce this result?
If you can answer all three using evidence from the model, you understand the main idea. If not, return to Explore and change one setting at a time.