AI Origins · Lesson 02
Live adversarial search lab: play Tic-Tac-Toe while the engine reveals maximizing/minimizing logic, terminal scoring, and game-tree decisions.
Play as X (maximizer). The AI plays O (minimizer) and returns best score + best move each turn using the minimax framework tied to von Neumann's 1928 theorem.
X turn · Maximizer
AI choice will appear here after search.
Blue nodes maximize, red nodes minimize, gold nodes are terminal outcomes.
Biography Spotlight
John von Neumann (1903–1957) was a Hungarian-American mathematician and polymath whose theoretical frameworks provide the literal blueprint for every AI you interact with today.
While he is famously known for the Von Neumann Architecture, the standard design of modern computers that separates processing from memory, his legacy in AI and robotics also comes from his obsession with self-replicating automata.
In 1928, he formalized the Minimax Theorem in game theory, showing how rational players choose the best strategy under opposition. That logic is still at the core of adversarial AI decision-making.
Now test that exact theory in action: play this Tic-Tac-Toe lab using minimax ideas developed by JVN in 1928.
Minimax is the decision rule behind many two-player game AIs: it looks ahead through possible moves, scores the endings, and then chooses the move that leads to the best worst-case outcome.
In a game like Tic-Tac-Toe, Minimax assumes that both players are trying to play as intelligently as possible. Instead of asking, "What move looks good right now?" the AI asks, "If I make this move, what will my opponent do next, and what happens after that?"
The algorithm explores the game tree one turn at a time until it reaches terminal states such as a win, loss, or draw. Those endings are given scores, and the scores are then passed back up the tree to evaluate earlier choices.
X can force a win.
Neither side can force victory.
O can force a win.
When it is the maximizing player's turn, the algorithm chooses the highest score available. When it is the minimizing player's turn, it chooses the lowest. That back-and-forth is why it is called Minimax: one player maximizes the score, while the other minimizes it.
Capture what the engine chose and why, then publish a short reflection with your demo link.