Artificial Intelligence
Build a graph, press play, and watch how each search algorithm decides what to explore next.
Research Team
The people behind these visualizers.
Jash Patel
pateljash0611@gmail.com
Mantram Joshi
mantramjoshi.079@gmail.comWhat is Artificial Intelligence?
Machines that perceive, reason, learn and act.
Artificial Intelligence (AI) is the branch of computer science that builds systems able to perform tasks that normally need human intelligence: understanding language, recognising images, making decisions and solving problems.
Many AI problems, such as route planning, puzzles and game playing, can be framed as search. The system starts in an initial state, applies actions, and looks through the space of possible states until it reaches a goal. Search algorithms differ in how they choose the next state, which changes their speed, memory use and whether the answer is optimal.
Uninformed algorithms (BFS, DFS, Uniform Cost) use only the problem definition. Informed algorithms (Greedy, A*) also use a heuristic, an estimate of how close a state is to the goal. Adversarial algorithms (Minimax) handle games where an opponent is working against you.
- Problem solving
- Machine learning
- Knowledge & reasoning
- Planning
- Natural language
- Computer vision
Traversal Visualizers
Pick an algorithm, then open its step-by-step visualizer.
Breadth-First Search
Explores a graph level by level using a FIFO queue, guaranteeing the fewest hops to the goal on unweighted graphs.
Open visualizer Stack · LIFODepth-First Search
Dives deep down one branch first before backtracking, using a stack to remember the way back to all unvisited nodes.
Open visualizer f = g + hA* Search
Combines path cost so far with a heuristic estimate to the goal, finding optimal paths when the heuristic is admissible.
Open visualizer f = hGreedy Best-First Search
Always expands the node that looks closest to the goal by heuristic alone, making it fast but not guaranteed optimal.
Open visualizer f = gUniform Cost Search
Expands the cheapest path found so far using a priority queue, guaranteeing the absolute lowest total cost on weighted graphs.
Open visualizer Min ↔ MaxMin/Max Search
Plays out a two-player game tree, alternating between a maximizing and a minimizing player, to pick the move with the best guaranteed outcome against an optimal opponent.
Open visualizer