Explore interactive visualizers for neural network training, Q-learning maze reinforcement, constraint satisfaction, and heuristic search.
Train deep feedforward networks with backpropagation, customize activation functions (ReLU, Sigmoid), and inspect decision boundaries.
w ← w - η(∂L/∂w)Train an autonomous agent to solve grid mazes using Bellman equation updates, exploration vs. exploitation (ε-greedy), and rewards.
Q(s, a) ← Q + α[R + γ max Q' - Q]Step through local heuristic gradient climbing; explore local maxima plateaus, ridge problems, and random-restart escapes.
s* = argmax_{s' ∈ N(s)} f(s')Solve N-Queens, map coloring, and Sudoku using Backtracking search with Forward Checking and AC-3 Arc Consistency.
CSP = ⟨X, D, C⟩Find optimal transfer sequences between unmeasured jugs using Breadth-First and Depth-First state search trees.
GCD(a, b) Divisibility LocusTrace deductive expert system inference rules, modus ponens derivations, and goal-directed backward query resolution.
P ⇒ Q, P ⊢ QFormulate STRIPS action operators (Push, Climb, Grasp) and step through automated Goal-Stack planning graphs.
STRIPS: ⟨Preconditions, Add, Delete⟩Calculate Bayesian letter probability distributions across dictionary corpuses to make optimal informational guesses.
P(Letter | Pattern, Excluded)Adjust learning rate, discount factor, and batch size to observe instant changes in model training stability.
Monitor convergence loss graphs, reward curves, and Q-table heatmaps dynamically.
Structured alongside university Artificial Intelligence (CS188 / CS221) and AP CS Principles.
Follow this standardized experimental methodology to configure parameters, simulate processes, and record scientific telemetry.
Choose from neural network classification, reinforcement Q-learning, constraint satisfaction, or heuristic search puzzles.
Adjust learning rates (η), discount factors (γ), epsilon exploration (ε), or initial puzzle starting configurations.
Watch agent exploration paths, loss curve convergence, backpropagation gradient vectors, or backtrack search trees update live.
Analyze reward convergence plots, classification confusion matrices, and optimal state action policy tables.
Bellman optimality, backpropagation gradient descent, and constraint propagation models evaluated in real time.
Our interactive AI problem suites cover fundamental topics in university CS Artificial Intelligence (Russell & Norvig curriculum), Machine Learning foundations, and heuristic optimization.
Students build deep conceptual models for state spaces, Q-learning policies, and backpropagation gradients through direct visual experimentation.
Inspect training loss curves, Q-value heatmaps, and search tree expansions in real time.
Technical and curriculum details about ai problems & machine learning.
The agent interacts with the maze environment through trial and error. By receiving rewards for reaching the goal and penalties for hitting walls, it uses the Bellman equation to update a Q-table that stores expected future rewards for every state-action pair.
Forward chaining is data-driven: it starts with known facts and applies inference rules to extract new facts until a goal is reached. Backward chaining is goal-driven: it starts with the hypothesis and searches for supporting facts in reverse.
CSP algorithms use Forward Checking and AC-3 Arc Consistency to prune invalid values from neighboring variables' domains before assigning them, cutting down exponential search spaces dramatically compared to naive brute-force.
Yes. All AI search solvers, neural network playgrounds, and reinforcement learning mazes are 100% free and open for educational use.