Step into the mind
of the algorithm.
Inspect decision boundaries, priority queues, and line-by-line state machines in real time. Built for AI engineers, researchers, and students.
How ThinkViz models intelligence.
Moving beyond static animations. Pure decoupled mathematical engines executing step-by-step state machines.
01 // Deterministic Graph Search
A*, BFS, DFS & DijkstraWatch admissible heuristics guide search rays through complex labyrinth networks. Every state expansion recalculates open/closed lists without black-box approximations.
02 // Live Metric Telemetry
Microsecond BenchmarksQuantify memory consumption, path cost ratios, and search tree depth frame-by-frame with tabular numeric precision.
03 // Adversarial Game Decision
Minimax & Alpha-BetaVisualize multi-agent zero-sum games. Watch alpha-beta pruning sever suboptimal decision branches before evaluation.
04 // Fitness Topography
Simulated Annealing & Genetic TSPExplore fitness landscapes, thermal annealing schedules, and genetic crossover operators solving NP-hard combinatorial optimization challenges.
Explore by category
Structured computational modules covering classical search, heuristic pathfinding, game trees, and machine learning.
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.
Adversarial Search
Decision-making algorithms for competitive zero-sum multi-agent games.
Rule-Based Systems
Knowledge stored as IF-THEN rules, reasoned over by an inference engine.
Machine Learning Suite
Explore K-Means clustering, analytical least-squares polynomial regression, and confusion matrix diagnostic simulators.
04 // BENCHMARKED ALGORITHM KERNELS
Featured Algorithms
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.
Minimax Algorithm (Minimax)
Determines optimal moves for zero-sum games by minimizing maximum possible opponent payoff.
Genetic Algorithm (Genetic Algo)
Simulates biological evolution through selection, crossover, and mutation to navigate deceptive multi-peak fitness landscapes.
Forward Chaining (Forward Chaining)
Data-driven inference starting with known facts and repeatedly firing rules to deduce conclusions.
Backward Chaining (Backward Chaining)
Goal-driven inference working backwards from a hypothesis to find supporting rules and facts.
Start exploring AI algorithms
Step through search, heuristic optimization, game trees, and machine learning simulators interactively. Build genuine mathematical intuition with deterministic zero-latency visual engines.