Game Theory

Counterfactual Regret Minimization (CFR) is an algorithm used in game theory to optimize decision-making in strategic environments.

Fictitious Play is a learning algorithm in game theory where players adjust strategies based on opponents' past actions.

Game Theory is the study of strategic interactions among rational decision-makers.

A game tree is a graphical representation of possible moves in a game, illustrating decision points and outcomes.

The Min-Max Theorem is a fundamental principle in game theory, establishing optimal strategies in zero-sum games.

Minimax Loss is a strategy in decision-making that aims to minimize the maximum possible loss.

The Minimax Principle is a decision-making strategy used in AI to minimize the possible loss in worst-case scenarios.

The Minimax Theorem is a fundamental principle in game theory, ensuring optimal strategies in zero-sum games.

Monte Carlo Tree Search (MCTS) is a method for decision-making in AI that uses random sampling to evaluate potential moves.

A Nash Equilibrium is a concept in game theory where no player can benefit by changing their strategy unilaterally.

An optimal strategy is the best plan or method for achieving a desired outcome in decision-making processes.

A Referee Agent is an AI system that monitors and enforces rules in competitive settings.