Visualize. Understand.
Think like AI.
Explore AI search algorithms, game decision trees, and optimization methods step-by-step with real-time state metrics and pseudocode tracking.
Explanation & Pseudocode
Evaluating node (2, 3) with minimum estimated cost f(n) = 8.82. Checking adjacent unvisited neighbors.
Learning Methodology
How ThinkViz works
Designed for intuition. Move beyond static diagrams and step through algorithms dynamically.
Select an algorithm
Choose from graph search, heuristic pathfinding, local optimization, or adversarial game decision trees.
Step through execution
Pause, step forward or backward, and inspect active nodes, priority queues, and line-by-line pseudocode.
Analyze metrics
Track nodes visited, cost functions, memory bounds, and execution state in real time.
Algorithm Domains
Explore by category
Structured modules covering classical search, heuristic pathfinding, and game trees.
Uninformed Search
Search algorithms that explore space without problem-specific domain heuristics.
Informed Search
Search algorithms that use heuristic evaluation to guide search towards goals faster.
Local Search
Iterative optimization algorithms operating on single active states.
Constraint Satisfaction
Algorithms finding states meeting a collection of strict variables and constraints.
Adversarial Search
Decision-making algorithms for competitive zero-sum multi-agent games.
Featured Algorithms
Core topics
Breadth-First Search (BFS)
Explores nodes level-by-level using a FIFO queue. Guarantees shortest path on unweighted graphs.
Depth-First Search (DFS)
Traverses graph branches as deep as possible before backtracking using a LIFO stack.
A* Search Algorithm (A*)
Combines path cost g(n) and heuristic h(n) to guarantee optimal search efficiency.
Hill Climbing (Hill Climbing)
Continually moves in the direction of increasing value/fitness to find local optima.
Start exploring AI algorithms
Step through search and optimization algorithms interactively. Build intuitive understanding for coursework and exams.